Background
There has been a recent increase in machine learning being used for
educational research and to inform educational practices. However, all
the data used is empirical and using this data risks privacy leaks. At
the same time, there have been great advances in the usage of purely
synthetic data to improve machine learning models and train models
without needing any empirical data. Our work in Nicolas and Sakib 2024
bridged this gap by creating a purely synthetic educational dataset to
improve educational research and practices. To learn more about this
work visit the github
page.
Data Generation Flowchart
The following flowchart, taken from the conference paper Nicolas and
Sakib 2024, shows the process used for generating each observation in
the dataset.
Data Generation Flowchart
Here we show the code used to generate a single observation which is
one student. This was run 100,000 times to generate the dataset this
work analyzes.
def generate_single_sample(self):
"""
Generate a single sample of synthetic data.
:return: Dictionary representing a single data sample.
"""
try:
first_name = random.choice(self.first_names)
last_name = random.choice(self.last_names)
ethnoracial_group = self.assign_demographic(self.ethnoracial_stats, self.ethnoracial_dist)
gender = self.assign_demographic(self.gender_stats, self.gender_dist)
international_status = self.assign_demographic(self.international_stats, self.international_dist)
socioeconomic_status = self.assign_demographic(self.socioeconomic_stats, self.socioeconomic_dist)
identities = [ethnoracial_group, gender, international_status]
learning_style = self.generate_learning_style()
student_semester = np.random.randint(0, 15)
gpa = self.generate_gpa(student_semester)
major = self.choose_major(student_semester, gender)
extracurricular_activities = self.generate_identity_org(identities)
previous_courses = []
subjects = []
career_aspirations_list = []
# Iterate through each semester the student has been at the school.
# Courses, subjects or interest, career aspirations and extracurriculars
# all interact with one another.
for semester in range(student_semester + 1):
previous_courses = self.generate_previous_courses(semester, learning_style, previous_courses, major)
num_courses = len(previous_courses)
top_subject, class_count = self.most_common_class_subject(previous_courses)
subjects = self.generate_subjects_of_interest(previous_courses, subjects, major, top_subject, career_aspirations_list)
career_aspirations_list = self.generate_career_aspirations(career_aspirations_list, subjects, major, extracurricular_activities)
extracurricular_activities = self.generate_extracurricular_activities(subjects, extracurricular_activities, major, career_aspirations_list)
# If the student has been there for 4 or more semesters and has enough courses,
# including those in their major then make them graduate by breaking the loop
if semester >= 8 and num_courses >= 32 and class_count >= 8:
student_semester = semester
break
if logging.getLogger().isEnabledFor(logging.DEBUG):
logging.debug("Iterated through %s semesters", student_semester)
# Split the tuples into course name, course type, and course subject
# Ex: Intro Asian American Studies, Discussion/Recitation, AAS
course_names = [course[0] for course in previous_courses]
course_type = list(set([tuple(course[2]) for course in previous_courses]))
course_subject = list(set([course[3] for course in previous_courses]))
# Create weighted lists based on how often an element was in a list
subjects, subject_weights = self.create_weighted_list(subjects)
extracurricular_activities, activity_weights = self.create_weighted_list(extracurricular_activities)
career_aspirations_list, career_weights = self.create_weighted_list(career_aspirations_list)
# Majors are weighted by semester and gpa
major_weights = [student_semester * gpa if student_semester != 0 else 0] * len(major)
# Generate the future topics
future_topics = self.generate_future_topics(subjects, subject_weights, extracurricular_activities, activity_weights, career_aspirations_list, career_weights, major, major_weights)
# Create the new 'student'
sample_data = {
'first name': first_name,
'last name': last_name,
'ethnoracial group': ethnoracial_group,
'gender': gender,
'international status': international_status,
'socioeconomic status': socioeconomic_status,
'learning style': learning_style,
'gpa': gpa,
'student semester': student_semester,
'major': major,
'previous courses': course_names,
'course types': course_type,
'course subjects': course_subject,
'subjects of interest': subjects,
'extracurricular activities': extracurricular_activities,
'career aspirations': career_aspirations_list,
'future topics': future_topics
}
if logging.getLogger().isEnabledFor(logging.DEBUG):
logging.debug("Single sample generated")
return sample_data
except Exception as e:
logging.error(f"Error generating sample: {e}")
return None
Motivating the Analysis
The work took this dataset and privatized it and then used machine
learning to predict the privacy-utility tradeoff of the different
privatization methods. However, the question of the accuracy of the
original purely synthetic dataset remains. This work addresses this
by running a variety of descriptive statistics on the dataset and
comparing the results to both the algorithm that produced the dataset
and the intended connections and distributions.
The Dataset
First we import the purely synthetic educational dataset and look at
the top 100 entries of the dataset:
dataset <- read_csv("Dataset.csv")
htmltools::div(
id = "unaltered-dataset-table",
DT::datatable(dataset[1:100, ],
options = list(pageLength = 1))
)
Now we do some minimal preprocessing to prepare the dataset for
analysis. The following code renames columns and converts some variables
to factors. A dictionary connecting actual column names with how they
would be written in a sentence is made using the Asai (2020) package.
dataset <- dataset |> # Rename the dataset columns and convert some to factors
rename(first_name = `first name`) |>
rename(last_name = `last name`) |>
rename(ethnoracial_group = `ethnoracial group`) |>
mutate(ethnoracial_group = as.factor(ethnoracial_group)) |>
mutate(gender = as.factor(gender)) |>
rename(international_status = `international status`) |>
mutate(international_status = as.factor(international_status)) |>
rename(socioeconomic_status = `socioeconomic status`) |>
mutate(socioeconomic_status = as.factor(socioeconomic_status)) |>
rename(learning_style = `learning style`) |>
rename(student_semester = `student semester`) |>
rename(previous_courses = `previous courses`) |>
rename(course_types = `course types`) |>
rename(course_subjects = `course subjects`) |>
rename(subjects_of_interest = `subjects of interest`) |>
rename(extracurricular_activities = `extracurricular activities`) |>
rename(career_aspirations = `career aspirations`) |>
rename(future_topics = `future topics`)
# Create a dataset of column names
dataset_col_names <- dict(
"Ethnoracial Group" = "ethnoracial_group",
"Gender" = "gender",
"International Student Status" = "international_status",
"Socioeconomic Status" = "socioeconomic_status",
"Learning Style" = "learning_style",
"GPA" = "gpa",
"GPA rounded to the nearest half" = "gpa_nearest_half",
"GPA rounded to the nearest integer" = "gpa_nearest_integer",
"Student Semester" = "student_semester",
"Student Year" = "student_year",
"Major" = "major",
"Previous Courses" = "previous_courses",
"Course Types" = "course_types",
"Course Subjects" = "course_subjects",
"Subjects of Interest" = "subjects_of_interest",
"Extracurricular Activities" = "extracurricular_activities",
"Career Aspirations" = "career_aspirations",
"Future Topics" = "future_topics"
)
Working with Numerical Variables
The range of GPA and Student Semester are too large for correlations
and comparisons. So here we make two new columns. One with GPA in
intervals of 0.5 and one with Student Year.
dataset <- dataset |>
# Count the number of observations with each value
group_by(gpa) |>
mutate(gpa_count = n()) |>
ungroup() |>
# Make the GPA tooltip text
mutate(text_gpa = paste("GPA", ": ", gpa, "\nCount: ", gpa_count, sep="")) |>
# Count the number of observations with each value
group_by(student_semester) |>
mutate(student_semester_count = n()) |>
ungroup() |>
# Make the Student Semester tooltip text
mutate(text_student_semester = paste("Student Semester", ": ", student_semester, "\nCount: ", student_semester_count, sep=""))
Function Design
Before we begin the analysis, we design functions to reduce
repetitiveness and length. The split_into_multiple function
was inspired by stack
exchange.
# This function makes correlation tables
correlation_table <- function(.data_1, .data_2) {
SES_tab <- table(.data_1,.data_2)
pilltabs(SES_tab)
}
# This function makes correlation graphs
correlation_graph <- function(x_name, y_name, flip = TRUE) {
# Get the x and y columns based on the inputed name
.x = dataset_col_names[x_name]
.y = dataset_col_names[y_name]
# Make the ggplot
plot <- ggplot(dataset, aes_string(x = .x,
fill = .y)) +
geom_bar(position = "fill") +
theme_classic() +
scale_fill_brewer(palette = "Dark2") +
labs(x = x_name,
y = "Proportion",
fill = y_name)
# Flip coordinates if flip is TRUE
if (flip) {
plot <- plot + coord_flip()
}
# Return the plot
return(plot)
}
# This function splits the list columns
split_into_multiple <- function(column, into_prefix, pattern = "\', \'"){
cols <- str_split_fixed(column, pattern, n = Inf)
# Sub out the ""'s returned by filling the matrix to the right, with NAs which are useful
cols[which(cols == "")] <- NA
cols <- as.tibble(cols)
# name the 'cols' tibble as 'into_prefix_1', 'into_prefix_2', ..., 'into_prefix_m'
# where m = # columns of 'cols'
m <- dim(cols)[2]
names(cols) <- paste(into_prefix, 1:m, sep = "_")
# Convert the tibble to a dataframe
cols <- as.data.frame(cols)
# Remove remaining brackets and quotes
for (i in 1:m) {
cols[,i] <- str_remove_all(cols[,i], "[\'\\[\\]]")
}
return(cols)
}
# Convert split list columns into a single dataframe with demographic information
make_demographic_dataframe <- function(main_df, .columns, .name, dem_cols = c(3:6), rm.na = FALSE, .name_list = list()) {
# Create an empty dataframe
final_df = data.frame(ethnoracial_group=factor(),
gender = factor(),
international_status = factor(),
socioeconomic_status = factor())
# Take each split list column and combine row wise
for (i in .columns) {
df <- main_df |>
select(c(dem_cols,i))
final_df <- rbind(final_df, setNames(df, colnames(final_df)))
}
# Add in extra names if more columns are added beyond the demographic ones
if (length(.name_list) > 0) {
for (i in 1:length(.name_list)) {
colnames(final_df)[i+4] <- .name_list[i]
}
}
# Ensure proper naming for the final column
colnames(final_df)[5+length(.name_list)] <- .name
# Drop the empty columns
if (rm.na) {
final_df <- final_df |>
drop_na()
}
return(final_df)
}
# Makes Graphs for the different numerical columns whose data
# was generated from a uniform distribution
numerical_col_graph <- function(.dataset, .column, .name, .text, .size) {
# Get the dataset size
row_len_df <- .dataset |>
drop_na({{.column}})
row_len <- nrow(row_len_df)
# Make the ggplot graph with a line for the uniform distribution
graph <- .dataset |>
# Make the ggplot
ggplot(aes(x = {{.column}}, text = {{.text}})) +
geom_bar(fill = "gray") +
theme_ipsum() +
labs(x = .name, y = "Number of Students") +
geom_hline(yintercept = row_len/.size) +
theme(legend.position="none")
# Make the plotly
ggplotly(graph, tooltip = "text")
}
# Generate a graph of the top ten majors with the highest majority of a certain
# gender identity
major_gender_dominated_graph <- function(.dataset, .gender, .xlab, .ylab) {
# Arrange major by top percent gender minority
g_dominate_dataset <- .dataset |>
arrange(desc({{.gender}}))
# Get a graph of the top ten majors
ggplot(g_dominate_dataset[1:10,],
aes(x=reorder(major, {{.gender}}, FUN = function(x) sum(x)),
y={{.gender}},
fill = division)) +
geom_bar(stat = "identity") +
coord_flip() +
theme_classic() +
scale_color_brewer(palette = "Dark2") +
labs(x = .xlab,
y = .ylab,
fill = "Major Division")
}
chisq_df_generation <- function(demographic_df, .column, expected_percent) {
# Count the number of students with each learning style
counts_df <- demographic_df |>
group_by({{.column}}) |>
drop_na() |>
summarize(count = n()) |>
ungroup()
# Combine the expected results with the counts Dataset
counts_df <- counts_df |>
mutate(observed_percent = count / nrow(demographic_df)) |>
cbind(expected_percent) |>
mutate(expected_percent = expected_percent / 100)
return(counts_df)
}
Spliting the Lists
Now we will use our split_into_multiple function to
split up the list features that we will use in this work.
dataset_split <- dataset |>
# Learning Style
cbind(split_into_multiple(dataset$learning_style, "learning_style")) |>
mutate(learning_style = NULL) |>
mutate(learning_style_1 = as.factor(learning_style_1)) |>
mutate(learning_style_2 = as.factor(learning_style_2)) |>
# Major
cbind(split_into_multiple(dataset$major, "major")) |>
mutate(major = NULL) |>
# Previous Course Types
cbind(split_into_multiple(dataset$course_types, "course_types")) |>
mutate(course_types = NULL) |>
# Previous Course Subjects
cbind(split_into_multiple(dataset$course_subjects, "course_subjects")) |>
mutate(course_subjects = NULL)
Demographics
In this section, we examine how close the observed demographic
variable distribution was to the expected real world distribution. This
is the code used to generate different demographic variables.
def assign_demographic(self, demographic_type, bias='Uniform'):
"""
Assign demographic attributes to students based on the specified type and bias.
:param demographic_type: Type of demographic attribute to assign (e.g., gender, ethnicity).
:param bias: The distribution bias to use for assignment.
:return: The assigned demographic value.
"""
demographics = list(demographic_type.keys())
if bias == 'real':
probabilities = [demographic_type[demo] / 100 for demo in demographics]
result = np.random.choice(demographics, p=probabilities)
elif bias == 'uniform':
result = np.random.choice(demographics)
return result
Ethnoracial Group
# Make the Counts Dataset
ethnoracial_group_counts <- chisq_df_generation(dataset, .column = ethnoracial_group, expected_percent = c(13.1, 0.7, 7.6, 53.4, 20.6, 4.3, 0.3))
This is the Chi Squared Goodness of Fit Test
# Chi Squared Goodness of Fit Test
chisq.test(x = ethnoracial_group_counts$observed_percent, p = ethnoracial_group_counts$expected_percent / sum(ethnoracial_group_counts$expected_percent))
##
## Chi-squared test for given probabilities
##
## data: ethnoracial_group_counts$observed_percent
## X-squared = 6.7565e-05, df = 6, p-value = 1
Gender
# Make the Counts Dataset
gender_counts <- chisq_df_generation(dataset, .column = gender, expected_percent = c(54.83, 40.07, 5.1))
This is the Chi Squared Goodness of Fit Test
# Chi Squared Goodness of Fit Test
chisq.test(x = gender_counts$observed_percent, p = gender_counts$expected_percent / sum(gender_counts$expected_percent))
##
## Chi-squared test for given probabilities
##
## data: gender_counts$observed_percent
## X-squared = 9.8661e-06, df = 2, p-value = 1
International Status
# Make the Counts Dataset
international_status_counts <- chisq_df_generation(dataset, .column = international_status, expected_percent = c(94.08, 5.92))
This is the Chi Squared Goodness of Fit Test
# Chi Squared Goodness of Fit Test
chisq.test(x = international_status_counts$observed_percent, p = international_status_counts$expected_percent / sum(international_status_counts$expected_percent))
##
## Chi-squared test for given probabilities
##
## data: international_status_counts$observed_percent
## X-squared = 1.4082e-07, df = 1, p-value = 0.9997
Socioeconomic Status
# Make the Counts Dataset
socioeconomic_status_counts <- chisq_df_generation(dataset, .column = socioeconomic_status, expected_percent = c(9, 20, 15, 37, 19))
This is the Chi Squared Goodness of Fit Test
# Chi Squared Goodness of Fit Test
chisq.test(x = socioeconomic_status_counts$observed_percent, p = socioeconomic_status_counts$expected_percent / sum(socioeconomic_status_counts$expected_percent))
##
## Chi-squared test for given probabilities
##
## data: socioeconomic_status_counts$observed_percent
## X-squared = 5.4584e-05, df = 4, p-value = 1
GPA
Here we show the function used to generate GPA. Notice that GPA is
not generated for 0th semester students, i.e. those that have not
started college yet.
def generate_gpa(self, semester):
"""
Generate a GPA for the student based on the semester.
:param semester: The semester number of the student.
:return: A GPA value.
"""
if semester == 0:
gpa = None
if logging.getLogger().isEnabledFor(logging.DEBUG):
logging.debug("GPA left empty because student semester is zero")
else:
gpa = round(np.random.uniform(2.0, 4.0), 2)
if logging.getLogger().isEnabledFor(logging.DEBUG):
logging.debug("GPA chosen: %.2f", gpa)
return gpa
GPA Graph
numerical_col_graph(dataset, gpa, "GPA", text_gpa, 201)
Student Semester
Notice that student semester was generated from a randomly from a
uniform distribution from 0 to 14.
student_semester = np.random.randint(0, 15)
numerical_col_graph(dataset, student_semester, "Student Semester", text_student_semester, 15)
Major
The code used in my summer research to determine major for each
student (observation) is shown here. Notice that the only features that
are direct inputs to the function are semester and gender. Thus we
expect that of all the demographic data only gender should be correlated
to major. Here are the demographics generated for each student:
- Ethnoracial Group
- Gender
- International Student Status
- Socioeconomic Status
def choose_major(self, semester, gender):
"""
Choose a major for the student based on the semester and gender.
:param semester: The semester number of the student.
:param gender: The gender of the student.
:return: A major.
"""
# Weigh majors by gender and popularity
major_weights = [
(1 - float(major[2]) / 173) + (1 - float(major[1]) / 100 if gender == 'Male' else float(major[1]) / 100)
for major in self.major_tuples
]
major = []
if semester <= 4:
if np.random.rand() < 0.3:
major.append(random.choices(self.major_list, major_weights, k=1)[0])
elif logging.getLogger().isEnabledFor(logging.DEBUG):
logging.debug("Major left empty because student has no major / intended major")
else:
major.append(random.choices(self.major_list, major_weights, k=1)[0])
if len(major) == 1 and np.random.rand() < 0.3:
extra_major = random.choices(self.major_list, major_weights, k=1)[0]
if extra_major not in major:
major.append(extra_major)
return major
Most Popular Majors
First, we look across majors and pull out the demographic data. Here
we list out the top 10 most popular majors. Data from the majors dataset
was webscrapped and and slightly altered from this website Economics (2024). The jsonlite package Ooms (2014) is used to import this data.
# Bring in the Majors Dataset which contains real world statistics
majors_data <- fromJSON("majors.json", flatten = TRUE)
# Rename the columns and convert the strings to numerical values and factors
majors_data <- as.data.frame(majors_data) |>
rename(major = V1) |>
rename(national_percent_female = V2) |>
mutate(national_percent_female = as.numeric(national_percent_female) / 100) |>
# Divide by 100 since the values are between 1 and 100 not 0 and 1
rename(national_popularity_ranking = V3) |>
mutate(national_popularity_ranking = as.numeric(national_popularity_ranking)) |>
rename(division = V4) |>
mutate(division = as.factor(division)) |>
rename(top_5_careers = V5) |>
mutate(top_5_careers = str_remove_all(top_5_careers, "[\'\\[\\]]"))
# Make the Major Demographics Dataset and join it to the Majors Data dataset
majors_demographics <- make_demographic_dataframe(dataset_split, c("major_1", "major_2"), "major", rm.na = TRUE) |>
group_by(major) |>
mutate(major = case_when(major == "" ~ NA, .default = major)) |>
ungroup()
# Create the Major Counts Dataset
major_counts <- majors_demographics |>
group_by(major) |>
drop_na() |>
summarize(major_count = n()) |>
ungroup()
# Add in the Majors Data to the Majors Counts Dataset
major_counts <- major_counts |>
left_join(majors_data, by = join_by(major)) |>
arrange(desc(major_count))
# Add in the observed popularity rankings to the Major Counts Dataset
popularity_ranking <- c(1:length(major_counts$major))
major_counts <- cbind(major_counts, popularity_ranking)
# Join Major Counts to Majors Demographics
majors_demographics <- majors_demographics |>
left_join(major_counts, by = join_by(major)) |>
arrange(desc(major_count))
# Plot the Top 10 Majors
ggplot(major_counts[1:10,], aes(x = reorder(major, major_count, FUN = function(x) sum(x)),
y = major_count,
fill = division)) +
coord_flip() +
theme_classic() +
scale_fill_viridis(discrete=TRUE) +
geom_bar(stat = "identity") +
labs(x = "Size of Major",
y = "Most Popular Majors",
fill = "Major Division")

The popularity ranking of a major impacts the major choice in the
major function. Thus we will compare our expected and observed major
popularity rankings to see if this aspect of the major function worked
as intended. Now we run a Chi Squared Goodness of Fit Test comparing the
observed major popularity rankings and expected major popularity
rankings.
# Chi Squared Goodness of Fit Test
observed_major_rankings <- major_counts[,7]
expected_major_rankings <- major_counts[,4]
chisq.test(x = observed_major_rankings, p = expected_major_rankings / sum(expected_major_rankings))
##
## Chi-squared test for given probabilities
##
## data: observed_major_rankings
## X-squared = 480.7, df = 172, p-value < 2.2e-16
This graph shows each major and compares observed major popularity vs
what was expected based on the national data.
p <- major_counts |>
# Make tooltip text
mutate(text = paste("Major: ", major, "\nNumber of Majors: ", major_count, "\nObserved Major Popularity Ranking: ", popularity_ranking, "\nNational Major Popularity Ranking: ", national_popularity_ranking, sep="")) |>
ggplot(aes(x = popularity_ranking,
y = national_popularity_ranking,
color = division,
text=text)) +
geom_point() +
theme_classic() +
scale_color_viridis(discrete=TRUE, guide=FALSE) +
labs(x = "Observed Major Popularity Ranking",
y = "Expected Major Popularity Ranking",
color = "Major Division")
# Make the plot interactive with ggplotly
ggplotly(p, tooltip="text")
Demographic Correlations
In this section, we examine the four demographics and see if they are
correlated to Major by running a Chi Squared Test of Independence
between each demographic and major choice.
Ethnoracial Group
The insignificant p-value means that there was no clear pattern
between ethnoracial group and major. Thus, ethnoracial group did not
impact the algorithm for major choice.
correlation_table(majors_demographics$ethnoracial_group, majors_demographics$major)
| African American or Black |
134 |
54 |
108 |
73 |
49 |
66 |
62 |
103 |
59 |
53 |
94 |
105 |
97 |
82 |
46 |
36 |
97 |
61 |
127 |
72 |
38 |
68 |
98 |
96 |
108 |
89 |
57 |
57 |
115 |
95 |
79 |
108 |
83 |
78 |
64 |
109 |
83 |
62 |
57 |
111 |
74 |
76 |
69 |
46 |
111 |
93 |
92 |
107 |
55 |
112 |
59 |
43 |
45 |
101 |
58 |
121 |
44 |
53 |
51 |
105 |
53 |
84 |
115 |
71 |
98 |
101 |
58 |
49 |
97 |
67 |
117 |
130 |
93 |
88 |
51 |
62 |
82 |
41 |
76 |
40 |
89 |
78 |
99 |
95 |
78 |
83 |
53 |
98 |
52 |
46 |
75 |
97 |
63 |
81 |
83 |
109 |
83 |
97 |
43 |
88 |
88 |
125 |
100 |
49 |
42 |
108 |
38 |
57 |
108 |
48 |
84 |
78 |
37 |
72 |
27 |
31 |
40 |
65 |
81 |
70 |
61 |
69 |
59 |
83 |
66 |
39 |
71 |
110 |
82 |
107 |
72 |
38 |
71 |
43 |
43 |
129 |
80 |
49 |
76 |
71 |
54 |
41 |
85 |
94 |
105 |
114 |
46 |
89 |
86 |
59 |
93 |
71 |
100 |
56 |
66 |
45 |
43 |
81 |
40 |
74 |
90 |
73 |
33 |
95 |
60 |
85 |
72 |
101 |
71 |
87 |
50 |
84 |
78 |
| American Indian or Alaska Native |
5 |
2 |
7 |
4 |
4 |
8 |
3 |
5 |
4 |
3 |
2 |
2 |
2 |
5 |
0 |
2 |
2 |
3 |
7 |
1 |
1 |
5 |
6 |
6 |
5 |
4 |
7 |
5 |
3 |
2 |
6 |
2 |
6 |
8 |
6 |
3 |
0 |
2 |
3 |
7 |
7 |
5 |
2 |
2 |
2 |
7 |
1 |
7 |
5 |
2 |
7 |
0 |
3 |
4 |
2 |
7 |
4 |
1 |
3 |
6 |
3 |
7 |
5 |
5 |
9 |
6 |
2 |
2 |
6 |
2 |
3 |
6 |
5 |
5 |
2 |
2 |
4 |
1 |
3 |
3 |
2 |
4 |
5 |
12 |
8 |
2 |
0 |
4 |
5 |
2 |
5 |
4 |
6 |
4 |
4 |
3 |
2 |
4 |
7 |
2 |
7 |
8 |
5 |
4 |
3 |
5 |
1 |
4 |
6 |
2 |
2 |
6 |
1 |
4 |
1 |
2 |
1 |
2 |
8 |
3 |
3 |
8 |
3 |
3 |
3 |
4 |
6 |
2 |
4 |
7 |
2 |
4 |
7 |
4 |
3 |
7 |
5 |
2 |
5 |
5 |
4 |
2 |
4 |
6 |
5 |
5 |
1 |
4 |
2 |
2 |
7 |
3 |
6 |
4 |
4 |
0 |
2 |
5 |
2 |
3 |
8 |
4 |
1 |
5 |
4 |
3 |
6 |
4 |
1 |
4 |
2 |
1 |
2 |
| Asian American |
64 |
18 |
42 |
30 |
26 |
37 |
51 |
48 |
34 |
27 |
57 |
47 |
54 |
46 |
21 |
26 |
35 |
30 |
66 |
28 |
23 |
54 |
63 |
60 |
37 |
53 |
33 |
34 |
53 |
54 |
42 |
64 |
56 |
50 |
37 |
51 |
58 |
34 |
30 |
74 |
54 |
37 |
35 |
19 |
55 |
50 |
57 |
56 |
31 |
70 |
32 |
22 |
22 |
54 |
39 |
70 |
25 |
28 |
37 |
45 |
42 |
56 |
58 |
50 |
64 |
60 |
31 |
24 |
55 |
42 |
61 |
54 |
60 |
49 |
52 |
31 |
44 |
20 |
40 |
24 |
48 |
44 |
55 |
46 |
51 |
41 |
43 |
55 |
29 |
22 |
36 |
58 |
32 |
49 |
52 |
57 |
59 |
47 |
28 |
45 |
44 |
64 |
52 |
31 |
26 |
54 |
11 |
36 |
46 |
28 |
42 |
51 |
25 |
45 |
23 |
20 |
26 |
43 |
44 |
41 |
34 |
30 |
30 |
40 |
37 |
22 |
49 |
53 |
52 |
58 |
46 |
28 |
48 |
20 |
21 |
74 |
38 |
36 |
53 |
38 |
34 |
27 |
48 |
76 |
43 |
59 |
20 |
53 |
47 |
35 |
61 |
49 |
70 |
32 |
45 |
19 |
36 |
44 |
24 |
44 |
58 |
59 |
16 |
40 |
34 |
55 |
53 |
40 |
35 |
65 |
31 |
55 |
32 |
| European American or white |
426 |
217 |
386 |
280 |
180 |
315 |
360 |
388 |
207 |
194 |
352 |
375 |
393 |
326 |
180 |
187 |
384 |
233 |
431 |
313 |
197 |
280 |
433 |
351 |
368 |
377 |
200 |
221 |
434 |
412 |
359 |
418 |
331 |
326 |
260 |
379 |
384 |
248 |
222 |
412 |
290 |
287 |
261 |
160 |
437 |
365 |
403 |
412 |
273 |
410 |
179 |
237 |
187 |
398 |
241 |
474 |
184 |
184 |
248 |
441 |
215 |
366 |
441 |
381 |
446 |
451 |
223 |
216 |
375 |
246 |
453 |
437 |
428 |
371 |
301 |
214 |
339 |
158 |
304 |
163 |
345 |
268 |
416 |
362 |
358 |
285 |
258 |
340 |
212 |
201 |
308 |
378 |
313 |
357 |
397 |
403 |
388 |
435 |
205 |
339 |
327 |
466 |
450 |
188 |
218 |
424 |
144 |
249 |
390 |
195 |
268 |
348 |
139 |
275 |
144 |
158 |
178 |
300 |
360 |
255 |
265 |
299 |
215 |
269 |
247 |
221 |
271 |
369 |
303 |
385 |
282 |
185 |
270 |
160 |
226 |
461 |
341 |
205 |
341 |
270 |
185 |
194 |
374 |
371 |
352 |
408 |
168 |
308 |
315 |
220 |
463 |
291 |
477 |
244 |
237 |
176 |
237 |
335 |
178 |
281 |
453 |
477 |
145 |
407 |
245 |
368 |
306 |
341 |
308 |
413 |
186 |
315 |
243 |
| Latino/a/x American |
162 |
84 |
159 |
115 |
69 |
115 |
138 |
163 |
105 |
81 |
159 |
143 |
141 |
148 |
77 |
75 |
132 |
80 |
158 |
136 |
78 |
105 |
181 |
139 |
142 |
157 |
78 |
81 |
166 |
143 |
140 |
184 |
135 |
131 |
99 |
156 |
142 |
88 |
78 |
182 |
140 |
110 |
97 |
65 |
160 |
129 |
145 |
183 |
89 |
171 |
73 |
79 |
78 |
142 |
112 |
193 |
90 |
71 |
91 |
162 |
80 |
150 |
146 |
130 |
181 |
169 |
99 |
74 |
154 |
126 |
167 |
153 |
160 |
144 |
110 |
76 |
108 |
49 |
125 |
76 |
133 |
107 |
154 |
137 |
156 |
120 |
97 |
142 |
68 |
97 |
116 |
137 |
104 |
139 |
143 |
162 |
142 |
162 |
71 |
131 |
137 |
164 |
144 |
74 |
84 |
146 |
61 |
84 |
167 |
61 |
120 |
132 |
60 |
126 |
64 |
53 |
77 |
105 |
120 |
96 |
107 |
117 |
84 |
113 |
102 |
82 |
116 |
139 |
117 |
188 |
113 |
67 |
85 |
61 |
67 |
175 |
125 |
79 |
128 |
108 |
78 |
78 |
131 |
139 |
120 |
161 |
47 |
118 |
135 |
91 |
176 |
96 |
208 |
103 |
73 |
81 |
105 |
139 |
57 |
133 |
168 |
171 |
56 |
151 |
89 |
134 |
124 |
144 |
122 |
177 |
71 |
146 |
100 |
| Multiracial |
35 |
12 |
32 |
24 |
17 |
24 |
29 |
42 |
15 |
22 |
24 |
27 |
19 |
30 |
18 |
19 |
25 |
22 |
47 |
21 |
11 |
27 |
28 |
26 |
41 |
35 |
18 |
13 |
28 |
28 |
22 |
39 |
25 |
28 |
27 |
25 |
27 |
12 |
27 |
34 |
27 |
21 |
22 |
4 |
43 |
24 |
25 |
22 |
28 |
30 |
9 |
20 |
10 |
26 |
15 |
34 |
16 |
16 |
22 |
34 |
23 |
42 |
35 |
34 |
31 |
29 |
16 |
13 |
28 |
24 |
43 |
35 |
38 |
28 |
21 |
20 |
24 |
15 |
28 |
11 |
28 |
19 |
40 |
28 |
32 |
30 |
15 |
28 |
19 |
15 |
21 |
31 |
14 |
24 |
25 |
29 |
29 |
35 |
21 |
23 |
33 |
33 |
38 |
24 |
20 |
28 |
11 |
22 |
29 |
17 |
21 |
25 |
12 |
18 |
11 |
10 |
14 |
20 |
24 |
21 |
31 |
27 |
26 |
24 |
21 |
15 |
26 |
22 |
26 |
18 |
26 |
8 |
20 |
12 |
14 |
31 |
32 |
13 |
23 |
23 |
16 |
12 |
27 |
33 |
26 |
25 |
11 |
15 |
25 |
14 |
32 |
13 |
36 |
24 |
19 |
14 |
23 |
27 |
6 |
19 |
37 |
41 |
5 |
20 |
20 |
28 |
14 |
24 |
26 |
33 |
8 |
28 |
25 |
| Pacific Islander |
2 |
3 |
2 |
2 |
1 |
0 |
0 |
3 |
1 |
2 |
2 |
3 |
2 |
3 |
2 |
0 |
1 |
1 |
3 |
1 |
1 |
4 |
3 |
3 |
3 |
1 |
1 |
3 |
1 |
1 |
1 |
3 |
1 |
2 |
0 |
2 |
1 |
1 |
1 |
1 |
0 |
2 |
1 |
0 |
4 |
1 |
4 |
1 |
0 |
4 |
0 |
1 |
2 |
0 |
3 |
5 |
1 |
1 |
3 |
3 |
2 |
2 |
2 |
1 |
6 |
5 |
2 |
1 |
0 |
2 |
1 |
1 |
3 |
2 |
0 |
1 |
2 |
0 |
4 |
0 |
1 |
0 |
2 |
5 |
1 |
1 |
2 |
2 |
0 |
3 |
2 |
1 |
1 |
2 |
3 |
1 |
1 |
3 |
1 |
2 |
1 |
3 |
1 |
0 |
1 |
3 |
0 |
0 |
2 |
1 |
3 |
3 |
1 |
1 |
2 |
1 |
1 |
1 |
2 |
0 |
6 |
0 |
2 |
1 |
0 |
1 |
2 |
2 |
2 |
1 |
1 |
0 |
2 |
2 |
0 |
3 |
4 |
0 |
2 |
1 |
3 |
3 |
1 |
2 |
0 |
2 |
0 |
3 |
2 |
3 |
2 |
0 |
1 |
2 |
1 |
1 |
2 |
3 |
0 |
1 |
3 |
2 |
1 |
0 |
1 |
2 |
2 |
1 |
2 |
4 |
0 |
2 |
0 |
| African American or Black |
1.0 |
0.4 |
0.8 |
0.6 |
0.4 |
0.5 |
0.5 |
0.8 |
0.5 |
0.4 |
0.7 |
0.8 |
0.7 |
0.6 |
0.4 |
0.3 |
0.7 |
0.5 |
1.0 |
0.5 |
0.3 |
0.5 |
0.7 |
0.7 |
0.8 |
0.7 |
0.4 |
0.4 |
0.9 |
0.7 |
0.6 |
0.8 |
0.6 |
0.6 |
0.5 |
0.8 |
0.6 |
0.5 |
0.4 |
0.8 |
0.6 |
0.6 |
0.5 |
0.4 |
0.8 |
0.7 |
0.7 |
0.8 |
0.4 |
0.9 |
0.5 |
0.3 |
0.3 |
0.8 |
0.4 |
0.9 |
0.3 |
0.4 |
0.4 |
0.8 |
0.4 |
0.6 |
0.9 |
0.5 |
0.7 |
0.8 |
0.4 |
0.4 |
0.7 |
0.5 |
0.9 |
1.0 |
0.7 |
0.7 |
0.4 |
0.5 |
0.6 |
0.3 |
0.6 |
0.3 |
0.7 |
0.6 |
0.8 |
0.7 |
0.6 |
0.6 |
0.4 |
0.7 |
0.4 |
0.4 |
0.6 |
0.7 |
0.5 |
0.6 |
0.6 |
0.8 |
0.6 |
0.7 |
0.3 |
0.7 |
0.7 |
1.0 |
0.8 |
0.4 |
0.3 |
0.8 |
0.3 |
0.4 |
0.8 |
0.4 |
0.6 |
0.6 |
0.3 |
0.5 |
0.2 |
0.2 |
0.3 |
0.5 |
0.6 |
0.5 |
0.5 |
0.5 |
0.5 |
0.6 |
0.5 |
0.3 |
0.5 |
0.8 |
0.6 |
0.8 |
0.5 |
0.3 |
0.5 |
0.3 |
0.3 |
1.0 |
0.6 |
0.4 |
0.6 |
0.5 |
0.4 |
0.3 |
0.6 |
0.7 |
0.8 |
0.9 |
0.4 |
0.7 |
0.7 |
0.5 |
0.7 |
0.5 |
0.8 |
0.4 |
0.5 |
0.3 |
0.3 |
0.6 |
0.3 |
0.6 |
0.7 |
0.6 |
0.3 |
0.7 |
0.5 |
0.6 |
0.5 |
0.8 |
0.5 |
0.7 |
0.4 |
0.6 |
0.6 |
100 |
13099 |
| American Indian or Alaska Native |
0.7 |
0.3 |
1.0 |
0.6 |
0.6 |
1.2 |
0.4 |
0.7 |
0.6 |
0.4 |
0.3 |
0.3 |
0.3 |
0.7 |
0.0 |
0.3 |
0.3 |
0.4 |
1.0 |
0.1 |
0.1 |
0.7 |
0.9 |
0.9 |
0.7 |
0.6 |
1.0 |
0.7 |
0.4 |
0.3 |
0.9 |
0.3 |
0.9 |
1.2 |
0.9 |
0.4 |
0.0 |
0.3 |
0.4 |
1.0 |
1.0 |
0.7 |
0.3 |
0.3 |
0.3 |
1.0 |
0.1 |
1.0 |
0.7 |
0.3 |
1.0 |
0.0 |
0.4 |
0.6 |
0.3 |
1.0 |
0.6 |
0.1 |
0.4 |
0.9 |
0.4 |
1.0 |
0.7 |
0.7 |
1.3 |
0.9 |
0.3 |
0.3 |
0.9 |
0.3 |
0.4 |
0.9 |
0.7 |
0.7 |
0.3 |
0.3 |
0.6 |
0.1 |
0.4 |
0.4 |
0.3 |
0.6 |
0.7 |
1.8 |
1.2 |
0.3 |
0.0 |
0.6 |
0.7 |
0.3 |
0.7 |
0.6 |
0.9 |
0.6 |
0.6 |
0.4 |
0.3 |
0.6 |
1.0 |
0.3 |
1.0 |
1.2 |
0.7 |
0.6 |
0.4 |
0.7 |
0.1 |
0.6 |
0.9 |
0.3 |
0.3 |
0.9 |
0.1 |
0.6 |
0.1 |
0.3 |
0.1 |
0.3 |
1.2 |
0.4 |
0.4 |
1.2 |
0.4 |
0.4 |
0.4 |
0.6 |
0.9 |
0.3 |
0.6 |
1.0 |
0.3 |
0.6 |
1.0 |
0.6 |
0.4 |
1.0 |
0.7 |
0.3 |
0.7 |
0.7 |
0.6 |
0.3 |
0.6 |
0.9 |
0.7 |
0.7 |
0.1 |
0.6 |
0.3 |
0.3 |
1.0 |
0.4 |
0.9 |
0.6 |
0.6 |
0.0 |
0.3 |
0.7 |
0.3 |
0.4 |
1.2 |
0.6 |
0.1 |
0.7 |
0.6 |
0.4 |
0.9 |
0.6 |
0.1 |
0.6 |
0.3 |
0.1 |
0.3 |
100 |
679 |
| Asian American |
0.9 |
0.2 |
0.6 |
0.4 |
0.4 |
0.5 |
0.7 |
0.6 |
0.5 |
0.4 |
0.8 |
0.6 |
0.7 |
0.6 |
0.3 |
0.4 |
0.5 |
0.4 |
0.9 |
0.4 |
0.3 |
0.7 |
0.9 |
0.8 |
0.5 |
0.7 |
0.4 |
0.5 |
0.7 |
0.7 |
0.6 |
0.9 |
0.8 |
0.7 |
0.5 |
0.7 |
0.8 |
0.5 |
0.4 |
1.0 |
0.7 |
0.5 |
0.5 |
0.3 |
0.7 |
0.7 |
0.8 |
0.8 |
0.4 |
0.9 |
0.4 |
0.3 |
0.3 |
0.7 |
0.5 |
0.9 |
0.3 |
0.4 |
0.5 |
0.6 |
0.6 |
0.8 |
0.8 |
0.7 |
0.9 |
0.8 |
0.4 |
0.3 |
0.7 |
0.6 |
0.8 |
0.7 |
0.8 |
0.7 |
0.7 |
0.4 |
0.6 |
0.3 |
0.5 |
0.3 |
0.6 |
0.6 |
0.7 |
0.6 |
0.7 |
0.6 |
0.6 |
0.7 |
0.4 |
0.3 |
0.5 |
0.8 |
0.4 |
0.7 |
0.7 |
0.8 |
0.8 |
0.6 |
0.4 |
0.6 |
0.6 |
0.9 |
0.7 |
0.4 |
0.4 |
0.7 |
0.1 |
0.5 |
0.6 |
0.4 |
0.6 |
0.7 |
0.3 |
0.6 |
0.3 |
0.3 |
0.4 |
0.6 |
0.6 |
0.6 |
0.5 |
0.4 |
0.4 |
0.5 |
0.5 |
0.3 |
0.7 |
0.7 |
0.7 |
0.8 |
0.6 |
0.4 |
0.6 |
0.3 |
0.3 |
1.0 |
0.5 |
0.5 |
0.7 |
0.5 |
0.5 |
0.4 |
0.6 |
1.0 |
0.6 |
0.8 |
0.3 |
0.7 |
0.6 |
0.5 |
0.8 |
0.7 |
0.9 |
0.4 |
0.6 |
0.3 |
0.5 |
0.6 |
0.3 |
0.6 |
0.8 |
0.8 |
0.2 |
0.5 |
0.5 |
0.7 |
0.7 |
0.5 |
0.5 |
0.9 |
0.4 |
0.7 |
0.4 |
100 |
7397 |
| European American or white |
0.8 |
0.4 |
0.7 |
0.5 |
0.3 |
0.6 |
0.7 |
0.7 |
0.4 |
0.4 |
0.7 |
0.7 |
0.7 |
0.6 |
0.3 |
0.4 |
0.7 |
0.4 |
0.8 |
0.6 |
0.4 |
0.5 |
0.8 |
0.7 |
0.7 |
0.7 |
0.4 |
0.4 |
0.8 |
0.8 |
0.7 |
0.8 |
0.6 |
0.6 |
0.5 |
0.7 |
0.7 |
0.5 |
0.4 |
0.8 |
0.5 |
0.5 |
0.5 |
0.3 |
0.8 |
0.7 |
0.8 |
0.8 |
0.5 |
0.8 |
0.3 |
0.4 |
0.4 |
0.8 |
0.5 |
0.9 |
0.3 |
0.3 |
0.5 |
0.8 |
0.4 |
0.7 |
0.8 |
0.7 |
0.8 |
0.9 |
0.4 |
0.4 |
0.7 |
0.5 |
0.9 |
0.8 |
0.8 |
0.7 |
0.6 |
0.4 |
0.6 |
0.3 |
0.6 |
0.3 |
0.7 |
0.5 |
0.8 |
0.7 |
0.7 |
0.5 |
0.5 |
0.6 |
0.4 |
0.4 |
0.6 |
0.7 |
0.6 |
0.7 |
0.7 |
0.8 |
0.7 |
0.8 |
0.4 |
0.6 |
0.6 |
0.9 |
0.8 |
0.4 |
0.4 |
0.8 |
0.3 |
0.5 |
0.7 |
0.4 |
0.5 |
0.7 |
0.3 |
0.5 |
0.3 |
0.3 |
0.3 |
0.6 |
0.7 |
0.5 |
0.5 |
0.6 |
0.4 |
0.5 |
0.5 |
0.4 |
0.5 |
0.7 |
0.6 |
0.7 |
0.5 |
0.3 |
0.5 |
0.3 |
0.4 |
0.9 |
0.6 |
0.4 |
0.6 |
0.5 |
0.3 |
0.4 |
0.7 |
0.7 |
0.7 |
0.8 |
0.3 |
0.6 |
0.6 |
0.4 |
0.9 |
0.5 |
0.9 |
0.5 |
0.4 |
0.3 |
0.4 |
0.6 |
0.3 |
0.5 |
0.9 |
0.9 |
0.3 |
0.8 |
0.5 |
0.7 |
0.6 |
0.6 |
0.6 |
0.8 |
0.4 |
0.6 |
0.5 |
100 |
52988 |
| Latino/a/x American |
0.8 |
0.4 |
0.8 |
0.6 |
0.3 |
0.6 |
0.7 |
0.8 |
0.5 |
0.4 |
0.8 |
0.7 |
0.7 |
0.7 |
0.4 |
0.4 |
0.6 |
0.4 |
0.8 |
0.7 |
0.4 |
0.5 |
0.9 |
0.7 |
0.7 |
0.8 |
0.4 |
0.4 |
0.8 |
0.7 |
0.7 |
0.9 |
0.7 |
0.6 |
0.5 |
0.8 |
0.7 |
0.4 |
0.4 |
0.9 |
0.7 |
0.5 |
0.5 |
0.3 |
0.8 |
0.6 |
0.7 |
0.9 |
0.4 |
0.8 |
0.4 |
0.4 |
0.4 |
0.7 |
0.5 |
0.9 |
0.4 |
0.3 |
0.4 |
0.8 |
0.4 |
0.7 |
0.7 |
0.6 |
0.9 |
0.8 |
0.5 |
0.4 |
0.7 |
0.6 |
0.8 |
0.7 |
0.8 |
0.7 |
0.5 |
0.4 |
0.5 |
0.2 |
0.6 |
0.4 |
0.6 |
0.5 |
0.7 |
0.7 |
0.8 |
0.6 |
0.5 |
0.7 |
0.3 |
0.5 |
0.6 |
0.7 |
0.5 |
0.7 |
0.7 |
0.8 |
0.7 |
0.8 |
0.3 |
0.6 |
0.7 |
0.8 |
0.7 |
0.4 |
0.4 |
0.7 |
0.3 |
0.4 |
0.8 |
0.3 |
0.6 |
0.6 |
0.3 |
0.6 |
0.3 |
0.3 |
0.4 |
0.5 |
0.6 |
0.5 |
0.5 |
0.6 |
0.4 |
0.5 |
0.5 |
0.4 |
0.6 |
0.7 |
0.6 |
0.9 |
0.5 |
0.3 |
0.4 |
0.3 |
0.3 |
0.8 |
0.6 |
0.4 |
0.6 |
0.5 |
0.4 |
0.4 |
0.6 |
0.7 |
0.6 |
0.8 |
0.2 |
0.6 |
0.7 |
0.4 |
0.9 |
0.5 |
1.0 |
0.5 |
0.4 |
0.4 |
0.5 |
0.7 |
0.3 |
0.6 |
0.8 |
0.8 |
0.3 |
0.7 |
0.4 |
0.7 |
0.6 |
0.7 |
0.6 |
0.9 |
0.3 |
0.7 |
0.5 |
100 |
20590 |
| Multiracial |
0.9 |
0.3 |
0.8 |
0.6 |
0.4 |
0.6 |
0.7 |
1.0 |
0.4 |
0.5 |
0.6 |
0.7 |
0.5 |
0.7 |
0.4 |
0.5 |
0.6 |
0.5 |
1.1 |
0.5 |
0.3 |
0.7 |
0.7 |
0.6 |
1.0 |
0.9 |
0.4 |
0.3 |
0.7 |
0.7 |
0.5 |
0.9 |
0.6 |
0.7 |
0.7 |
0.6 |
0.7 |
0.3 |
0.7 |
0.8 |
0.7 |
0.5 |
0.5 |
0.1 |
1.0 |
0.6 |
0.6 |
0.5 |
0.7 |
0.7 |
0.2 |
0.5 |
0.2 |
0.6 |
0.4 |
0.8 |
0.4 |
0.4 |
0.5 |
0.8 |
0.6 |
1.0 |
0.9 |
0.8 |
0.8 |
0.7 |
0.4 |
0.3 |
0.7 |
0.6 |
1.0 |
0.9 |
0.9 |
0.7 |
0.5 |
0.5 |
0.6 |
0.4 |
0.7 |
0.3 |
0.7 |
0.5 |
1.0 |
0.7 |
0.8 |
0.7 |
0.4 |
0.7 |
0.5 |
0.4 |
0.5 |
0.8 |
0.3 |
0.6 |
0.6 |
0.7 |
0.7 |
0.9 |
0.5 |
0.6 |
0.8 |
0.8 |
0.9 |
0.6 |
0.5 |
0.7 |
0.3 |
0.5 |
0.7 |
0.4 |
0.5 |
0.6 |
0.3 |
0.4 |
0.3 |
0.2 |
0.3 |
0.5 |
0.6 |
0.5 |
0.8 |
0.7 |
0.6 |
0.6 |
0.5 |
0.4 |
0.6 |
0.5 |
0.6 |
0.4 |
0.6 |
0.2 |
0.5 |
0.3 |
0.3 |
0.8 |
0.8 |
0.3 |
0.6 |
0.6 |
0.4 |
0.3 |
0.7 |
0.8 |
0.6 |
0.6 |
0.3 |
0.4 |
0.6 |
0.3 |
0.8 |
0.3 |
0.9 |
0.6 |
0.5 |
0.3 |
0.6 |
0.7 |
0.1 |
0.5 |
0.9 |
1.0 |
0.1 |
0.5 |
0.5 |
0.7 |
0.3 |
0.6 |
0.6 |
0.8 |
0.2 |
0.7 |
0.6 |
100 |
4112 |
| Pacific Islander |
0.7 |
1.0 |
0.7 |
0.7 |
0.3 |
0.0 |
0.0 |
1.0 |
0.3 |
0.7 |
0.7 |
1.0 |
0.7 |
1.0 |
0.7 |
0.0 |
0.3 |
0.3 |
1.0 |
0.3 |
0.3 |
1.4 |
1.0 |
1.0 |
1.0 |
0.3 |
0.3 |
1.0 |
0.3 |
0.3 |
0.3 |
1.0 |
0.3 |
0.7 |
0.0 |
0.7 |
0.3 |
0.3 |
0.3 |
0.3 |
0.0 |
0.7 |
0.3 |
0.0 |
1.4 |
0.3 |
1.4 |
0.3 |
0.0 |
1.4 |
0.0 |
0.3 |
0.7 |
0.0 |
1.0 |
1.7 |
0.3 |
0.3 |
1.0 |
1.0 |
0.7 |
0.7 |
0.7 |
0.3 |
2.1 |
1.7 |
0.7 |
0.3 |
0.0 |
0.7 |
0.3 |
0.3 |
1.0 |
0.7 |
0.0 |
0.3 |
0.7 |
0.0 |
1.4 |
0.0 |
0.3 |
0.0 |
0.7 |
1.7 |
0.3 |
0.3 |
0.7 |
0.7 |
0.0 |
1.0 |
0.7 |
0.3 |
0.3 |
0.7 |
1.0 |
0.3 |
0.3 |
1.0 |
0.3 |
0.7 |
0.3 |
1.0 |
0.3 |
0.0 |
0.3 |
1.0 |
0.0 |
0.0 |
0.7 |
0.3 |
1.0 |
1.0 |
0.3 |
0.3 |
0.7 |
0.3 |
0.3 |
0.3 |
0.7 |
0.0 |
2.1 |
0.0 |
0.7 |
0.3 |
0.0 |
0.3 |
0.7 |
0.7 |
0.7 |
0.3 |
0.3 |
0.0 |
0.7 |
0.7 |
0.0 |
1.0 |
1.4 |
0.0 |
0.7 |
0.3 |
1.0 |
1.0 |
0.3 |
0.7 |
0.0 |
0.7 |
0.0 |
1.0 |
0.7 |
1.0 |
0.7 |
0.0 |
0.3 |
0.7 |
0.3 |
0.3 |
0.7 |
1.0 |
0.0 |
0.3 |
1.0 |
0.7 |
0.3 |
0.0 |
0.3 |
0.7 |
0.7 |
0.3 |
0.7 |
1.4 |
0.0 |
0.7 |
0.0 |
100 |
286 |
| All |
0.8 |
0.4 |
0.7 |
0.5 |
0.3 |
0.6 |
0.6 |
0.8 |
0.4 |
0.4 |
0.7 |
0.7 |
0.7 |
0.6 |
0.3 |
0.3 |
0.7 |
0.4 |
0.8 |
0.6 |
0.4 |
0.5 |
0.8 |
0.7 |
0.7 |
0.7 |
0.4 |
0.4 |
0.8 |
0.7 |
0.7 |
0.8 |
0.6 |
0.6 |
0.5 |
0.7 |
0.7 |
0.5 |
0.4 |
0.8 |
0.6 |
0.5 |
0.5 |
0.3 |
0.8 |
0.7 |
0.7 |
0.8 |
0.5 |
0.8 |
0.4 |
0.4 |
0.3 |
0.7 |
0.5 |
0.9 |
0.4 |
0.4 |
0.5 |
0.8 |
0.4 |
0.7 |
0.8 |
0.7 |
0.8 |
0.8 |
0.4 |
0.4 |
0.7 |
0.5 |
0.9 |
0.8 |
0.8 |
0.7 |
0.5 |
0.4 |
0.6 |
0.3 |
0.6 |
0.3 |
0.7 |
0.5 |
0.8 |
0.7 |
0.7 |
0.6 |
0.5 |
0.7 |
0.4 |
0.4 |
0.6 |
0.7 |
0.5 |
0.7 |
0.7 |
0.8 |
0.7 |
0.8 |
0.4 |
0.6 |
0.6 |
0.9 |
0.8 |
0.4 |
0.4 |
0.8 |
0.3 |
0.5 |
0.8 |
0.4 |
0.5 |
0.6 |
0.3 |
0.5 |
0.3 |
0.3 |
0.3 |
0.5 |
0.6 |
0.5 |
0.5 |
0.6 |
0.4 |
0.5 |
0.5 |
0.4 |
0.5 |
0.7 |
0.6 |
0.8 |
0.5 |
0.3 |
0.5 |
0.3 |
0.4 |
0.9 |
0.6 |
0.4 |
0.6 |
0.5 |
0.4 |
0.4 |
0.7 |
0.7 |
0.7 |
0.8 |
0.3 |
0.6 |
0.6 |
0.4 |
0.8 |
0.5 |
0.9 |
0.5 |
0.4 |
0.3 |
0.5 |
0.6 |
0.3 |
0.6 |
0.8 |
0.8 |
0.3 |
0.7 |
0.5 |
0.7 |
0.6 |
0.7 |
0.6 |
0.8 |
0.4 |
0.6 |
0.5 |
100 |
99151 |
| African American or Black |
16.2 |
13.8 |
14.7 |
13.8 |
14.2 |
11.7 |
9.6 |
13.7 |
13.9 |
13.9 |
13.6 |
15.0 |
13.7 |
12.8 |
13.4 |
10.4 |
14.3 |
14.2 |
15.1 |
12.6 |
10.9 |
12.5 |
12.1 |
14.1 |
15.3 |
12.4 |
14.5 |
13.8 |
14.4 |
12.9 |
12.2 |
13.2 |
13.0 |
12.5 |
13.0 |
15.0 |
11.9 |
13.9 |
13.6 |
13.5 |
12.5 |
14.1 |
14.2 |
15.5 |
13.7 |
13.9 |
12.7 |
13.6 |
11.4 |
14.0 |
16.4 |
10.7 |
13.0 |
13.9 |
12.3 |
13.4 |
12.1 |
15.0 |
11.2 |
13.2 |
12.7 |
11.9 |
14.3 |
10.6 |
11.7 |
12.3 |
13.5 |
12.9 |
13.6 |
13.2 |
13.8 |
15.9 |
11.8 |
12.8 |
9.5 |
15.3 |
13.6 |
14.4 |
13.1 |
12.6 |
13.8 |
15.0 |
12.8 |
13.9 |
11.4 |
14.8 |
11.3 |
14.6 |
13.5 |
11.9 |
13.3 |
13.7 |
11.8 |
12.3 |
11.7 |
14.3 |
11.8 |
12.4 |
11.4 |
14.0 |
13.8 |
14.5 |
12.7 |
13.2 |
10.7 |
14.1 |
14.3 |
12.6 |
14.4 |
13.6 |
15.6 |
12.1 |
13.5 |
13.3 |
9.9 |
11.3 |
11.9 |
12.1 |
12.7 |
14.4 |
12.0 |
12.5 |
14.1 |
15.6 |
13.9 |
10.2 |
13.1 |
15.8 |
14.0 |
14.0 |
13.3 |
11.5 |
14.1 |
14.2 |
11.5 |
14.7 |
12.8 |
12.8 |
12.1 |
13.8 |
14.4 |
11.5 |
12.7 |
13.0 |
16.1 |
14.7 |
15.7 |
15.1 |
14.1 |
13.9 |
11.2 |
13.6 |
11.1 |
12.0 |
14.8 |
13.4 |
9.6 |
12.8 |
13.0 |
13.3 |
11.0 |
8.8 |
12.8 |
13.2 |
13.2 |
12.6 |
12.5 |
15.4 |
12.6 |
11.1 |
14.4 |
13.3 |
16.2 |
13.2 |
| American Indian or Alaska Native |
0.6 |
0.5 |
1.0 |
0.8 |
1.2 |
1.4 |
0.5 |
0.7 |
0.9 |
0.8 |
0.3 |
0.3 |
0.3 |
0.8 |
0.0 |
0.6 |
0.3 |
0.7 |
0.8 |
0.2 |
0.3 |
0.9 |
0.7 |
0.9 |
0.7 |
0.6 |
1.8 |
1.2 |
0.4 |
0.3 |
0.9 |
0.2 |
0.9 |
1.3 |
1.2 |
0.4 |
0.0 |
0.4 |
0.7 |
0.9 |
1.2 |
0.9 |
0.4 |
0.7 |
0.2 |
1.0 |
0.1 |
0.9 |
1.0 |
0.3 |
1.9 |
0.0 |
0.9 |
0.6 |
0.4 |
0.8 |
1.1 |
0.3 |
0.7 |
0.8 |
0.7 |
1.0 |
0.6 |
0.7 |
1.1 |
0.7 |
0.5 |
0.5 |
0.8 |
0.4 |
0.4 |
0.7 |
0.6 |
0.7 |
0.4 |
0.5 |
0.7 |
0.4 |
0.5 |
0.9 |
0.3 |
0.8 |
0.6 |
1.8 |
1.2 |
0.4 |
0.0 |
0.6 |
1.3 |
0.5 |
0.9 |
0.6 |
1.1 |
0.6 |
0.6 |
0.4 |
0.3 |
0.5 |
1.9 |
0.3 |
1.1 |
0.9 |
0.6 |
1.1 |
0.8 |
0.7 |
0.4 |
0.9 |
0.8 |
0.6 |
0.4 |
0.9 |
0.4 |
0.7 |
0.4 |
0.7 |
0.3 |
0.4 |
1.3 |
0.6 |
0.6 |
1.5 |
0.7 |
0.6 |
0.6 |
1.0 |
1.1 |
0.3 |
0.7 |
0.9 |
0.4 |
1.2 |
1.4 |
1.3 |
0.8 |
0.8 |
0.8 |
0.5 |
0.8 |
1.0 |
1.1 |
0.6 |
0.6 |
0.8 |
0.8 |
0.6 |
0.3 |
0.7 |
0.3 |
0.5 |
0.8 |
0.6 |
0.7 |
0.9 |
0.9 |
0.0 |
0.4 |
0.8 |
0.7 |
0.5 |
1.0 |
0.5 |
0.4 |
0.7 |
0.9 |
0.4 |
1.0 |
0.6 |
0.2 |
0.5 |
0.6 |
0.2 |
0.4 |
0.7 |
| Asian American |
7.7 |
4.6 |
5.7 |
5.7 |
7.5 |
6.5 |
7.9 |
6.4 |
8.0 |
7.1 |
8.3 |
6.7 |
7.6 |
7.2 |
6.1 |
7.5 |
5.2 |
7.0 |
7.9 |
4.9 |
6.6 |
9.9 |
7.8 |
8.8 |
5.3 |
7.4 |
8.4 |
8.2 |
6.6 |
7.3 |
6.5 |
7.8 |
8.8 |
8.0 |
7.5 |
7.0 |
8.3 |
7.6 |
7.2 |
9.0 |
9.1 |
6.9 |
7.2 |
6.4 |
6.8 |
7.5 |
7.8 |
7.1 |
6.4 |
8.8 |
8.9 |
5.5 |
6.3 |
7.4 |
8.3 |
7.7 |
6.9 |
7.9 |
8.1 |
5.7 |
10.0 |
7.9 |
7.2 |
7.4 |
7.7 |
7.3 |
7.2 |
6.3 |
7.7 |
8.3 |
7.2 |
6.6 |
7.6 |
7.1 |
9.7 |
7.6 |
7.3 |
7.0 |
6.9 |
7.6 |
7.4 |
8.5 |
7.1 |
6.7 |
7.5 |
7.3 |
9.2 |
8.2 |
7.5 |
5.7 |
6.4 |
8.2 |
6.0 |
7.5 |
7.4 |
7.5 |
8.4 |
6.0 |
7.4 |
7.1 |
6.9 |
7.4 |
6.6 |
8.4 |
6.6 |
7.0 |
4.1 |
8.0 |
6.1 |
8.0 |
7.8 |
7.9 |
9.1 |
8.3 |
8.5 |
7.3 |
7.7 |
8.0 |
6.9 |
8.4 |
6.7 |
5.5 |
7.2 |
7.5 |
7.8 |
5.7 |
9.1 |
7.6 |
8.9 |
7.6 |
8.5 |
8.5 |
9.5 |
6.6 |
5.6 |
8.4 |
6.1 |
9.4 |
8.4 |
7.4 |
9.1 |
7.6 |
7.2 |
10.5 |
6.6 |
7.6 |
6.8 |
9.0 |
7.7 |
8.3 |
7.3 |
9.4 |
7.8 |
6.9 |
10.1 |
5.7 |
8.0 |
6.9 |
7.8 |
7.9 |
7.1 |
7.1 |
6.2 |
5.6 |
7.5 |
8.1 |
9.2 |
6.1 |
6.2 |
8.3 |
8.9 |
8.7 |
6.7 |
7.5 |
| European American or white |
51.4 |
55.6 |
52.4 |
53.0 |
52.0 |
55.8 |
56.0 |
51.6 |
48.7 |
50.8 |
51.0 |
53.4 |
55.5 |
50.9 |
52.3 |
54.2 |
56.8 |
54.2 |
51.4 |
54.7 |
56.4 |
51.6 |
53.3 |
51.5 |
52.3 |
52.7 |
50.8 |
53.4 |
54.2 |
56.1 |
55.3 |
51.1 |
52.0 |
52.3 |
52.7 |
52.3 |
55.3 |
55.5 |
53.1 |
50.2 |
49.0 |
53.3 |
53.6 |
54.1 |
53.8 |
54.6 |
55.4 |
52.3 |
56.8 |
51.3 |
49.9 |
59.0 |
53.9 |
54.9 |
51.3 |
52.4 |
50.5 |
52.0 |
54.5 |
55.4 |
51.4 |
51.8 |
55.0 |
56.7 |
53.4 |
54.9 |
51.7 |
57.0 |
52.4 |
48.3 |
53.6 |
53.6 |
54.4 |
54.0 |
56.1 |
52.7 |
56.2 |
55.6 |
52.4 |
51.4 |
53.4 |
51.5 |
54.0 |
52.8 |
52.3 |
50.7 |
55.1 |
50.8 |
55.1 |
52.1 |
54.7 |
53.5 |
58.7 |
54.4 |
56.2 |
52.7 |
55.1 |
55.6 |
54.5 |
53.8 |
51.3 |
54.0 |
57.0 |
50.8 |
55.3 |
55.2 |
54.1 |
55.1 |
52.1 |
55.4 |
49.6 |
54.1 |
50.5 |
50.8 |
52.9 |
57.5 |
52.8 |
56.0 |
56.3 |
52.5 |
52.3 |
54.4 |
51.3 |
50.5 |
51.9 |
57.6 |
50.1 |
52.9 |
51.7 |
50.4 |
52.0 |
56.1 |
53.7 |
53.0 |
60.4 |
52.4 |
54.6 |
53.4 |
54.3 |
52.3 |
49.5 |
54.3 |
55.8 |
51.5 |
54.1 |
52.7 |
57.3 |
52.2 |
51.5 |
51.9 |
55.5 |
55.6 |
53.1 |
52.5 |
53.3 |
52.4 |
52.9 |
52.8 |
58.0 |
50.6 |
55.4 |
57.7 |
56.4 |
56.7 |
54.1 |
54.5 |
53.0 |
52.1 |
54.5 |
52.7 |
53.4 |
49.9 |
50.6 |
53.4 |
| Latino/a/x American |
19.6 |
21.5 |
21.6 |
21.8 |
19.9 |
20.4 |
21.5 |
21.7 |
24.7 |
21.2 |
23.0 |
20.4 |
19.9 |
23.1 |
22.4 |
21.7 |
19.5 |
18.6 |
18.8 |
23.8 |
22.3 |
19.3 |
22.3 |
20.4 |
20.2 |
21.9 |
19.8 |
19.6 |
20.8 |
19.5 |
21.6 |
22.5 |
21.2 |
21.0 |
20.1 |
21.5 |
20.4 |
19.7 |
18.7 |
22.2 |
23.6 |
20.4 |
19.9 |
22.0 |
19.7 |
19.3 |
19.9 |
23.2 |
18.5 |
21.4 |
20.3 |
19.7 |
22.5 |
19.6 |
23.8 |
21.3 |
24.7 |
20.1 |
20.0 |
20.4 |
19.1 |
21.2 |
18.2 |
19.3 |
21.7 |
20.6 |
23.0 |
19.5 |
21.5 |
24.8 |
19.8 |
18.8 |
20.3 |
21.0 |
20.5 |
18.7 |
17.9 |
17.3 |
21.6 |
24.0 |
20.6 |
20.6 |
20.0 |
20.0 |
22.8 |
21.4 |
20.7 |
21.2 |
17.7 |
25.1 |
20.6 |
19.4 |
19.5 |
21.2 |
20.2 |
21.2 |
20.2 |
20.7 |
18.9 |
20.8 |
21.5 |
19.0 |
18.2 |
20.0 |
21.3 |
19.0 |
22.9 |
18.6 |
22.3 |
17.3 |
22.2 |
20.5 |
21.8 |
23.3 |
23.5 |
19.3 |
22.8 |
19.6 |
18.8 |
19.8 |
21.1 |
21.3 |
20.0 |
21.2 |
21.4 |
21.4 |
21.4 |
19.9 |
20.0 |
24.6 |
20.8 |
20.3 |
16.9 |
20.2 |
17.9 |
19.9 |
20.0 |
20.6 |
20.4 |
20.9 |
20.9 |
21.8 |
19.6 |
19.3 |
18.4 |
20.8 |
16.0 |
20.0 |
22.1 |
21.5 |
21.1 |
18.4 |
23.2 |
22.2 |
16.4 |
24.1 |
23.4 |
21.9 |
18.6 |
24.0 |
20.6 |
20.7 |
21.8 |
21.0 |
19.6 |
19.9 |
21.5 |
22.0 |
21.6 |
22.6 |
20.4 |
23.1 |
20.8 |
20.8 |
| Multiracial |
4.2 |
3.1 |
4.3 |
4.5 |
4.9 |
4.2 |
4.5 |
5.6 |
3.5 |
5.8 |
3.5 |
3.8 |
2.7 |
4.7 |
5.2 |
5.5 |
3.7 |
5.1 |
5.6 |
3.7 |
3.2 |
5.0 |
3.4 |
3.8 |
5.8 |
4.9 |
4.6 |
3.1 |
3.5 |
3.8 |
3.4 |
4.8 |
3.9 |
4.5 |
5.5 |
3.4 |
3.9 |
2.7 |
6.5 |
4.1 |
4.6 |
3.9 |
4.5 |
1.4 |
5.3 |
3.6 |
3.4 |
2.8 |
5.8 |
3.8 |
2.5 |
5.0 |
2.9 |
3.6 |
3.2 |
3.8 |
4.4 |
4.5 |
4.8 |
4.3 |
5.5 |
5.9 |
4.4 |
5.1 |
3.7 |
3.5 |
3.7 |
3.4 |
3.9 |
4.7 |
5.1 |
4.3 |
4.8 |
4.1 |
3.9 |
4.9 |
4.0 |
5.3 |
4.8 |
3.5 |
4.3 |
3.7 |
5.2 |
4.1 |
4.7 |
5.3 |
3.2 |
4.2 |
4.9 |
3.9 |
3.7 |
4.4 |
2.6 |
3.7 |
3.5 |
3.8 |
4.1 |
4.5 |
5.6 |
3.7 |
5.2 |
3.8 |
4.8 |
6.5 |
5.1 |
3.6 |
4.1 |
4.9 |
3.9 |
4.8 |
3.9 |
3.9 |
4.4 |
3.3 |
4.0 |
3.6 |
4.2 |
3.7 |
3.8 |
4.3 |
6.1 |
4.9 |
6.2 |
4.5 |
4.4 |
3.9 |
4.8 |
3.2 |
4.4 |
2.4 |
4.8 |
2.4 |
4.0 |
4.0 |
3.7 |
3.5 |
5.1 |
3.4 |
3.7 |
4.5 |
4.3 |
3.4 |
4.0 |
4.6 |
4.0 |
3.2 |
3.8 |
2.5 |
4.1 |
3.3 |
3.8 |
2.5 |
4.0 |
5.2 |
4.3 |
4.2 |
5.1 |
4.3 |
2.0 |
3.4 |
4.5 |
5.0 |
1.9 |
2.8 |
4.4 |
4.1 |
2.4 |
3.7 |
4.6 |
4.2 |
2.3 |
4.4 |
5.2 |
4.1 |
| Pacific Islander |
0.2 |
0.8 |
0.3 |
0.4 |
0.3 |
0.0 |
0.0 |
0.4 |
0.2 |
0.5 |
0.3 |
0.4 |
0.3 |
0.5 |
0.6 |
0.0 |
0.1 |
0.2 |
0.4 |
0.2 |
0.3 |
0.7 |
0.4 |
0.4 |
0.4 |
0.1 |
0.3 |
0.7 |
0.1 |
0.1 |
0.2 |
0.4 |
0.2 |
0.3 |
0.0 |
0.3 |
0.1 |
0.2 |
0.2 |
0.1 |
0.0 |
0.4 |
0.2 |
0.0 |
0.5 |
0.1 |
0.6 |
0.1 |
0.0 |
0.5 |
0.0 |
0.2 |
0.6 |
0.0 |
0.6 |
0.6 |
0.3 |
0.3 |
0.7 |
0.4 |
0.5 |
0.3 |
0.2 |
0.1 |
0.7 |
0.6 |
0.5 |
0.3 |
0.0 |
0.4 |
0.1 |
0.1 |
0.4 |
0.3 |
0.0 |
0.2 |
0.3 |
0.0 |
0.7 |
0.0 |
0.2 |
0.0 |
0.3 |
0.7 |
0.1 |
0.2 |
0.4 |
0.3 |
0.0 |
0.8 |
0.4 |
0.1 |
0.2 |
0.3 |
0.4 |
0.1 |
0.1 |
0.4 |
0.3 |
0.3 |
0.2 |
0.3 |
0.1 |
0.0 |
0.3 |
0.4 |
0.0 |
0.0 |
0.3 |
0.3 |
0.6 |
0.5 |
0.4 |
0.2 |
0.7 |
0.4 |
0.3 |
0.2 |
0.3 |
0.0 |
1.2 |
0.0 |
0.5 |
0.2 |
0.0 |
0.3 |
0.4 |
0.3 |
0.3 |
0.1 |
0.2 |
0.0 |
0.4 |
0.7 |
0.0 |
0.3 |
0.6 |
0.0 |
0.3 |
0.2 |
0.8 |
0.8 |
0.1 |
0.3 |
0.0 |
0.3 |
0.0 |
0.5 |
0.3 |
0.7 |
0.2 |
0.0 |
0.1 |
0.4 |
0.2 |
0.3 |
0.4 |
0.5 |
0.0 |
0.2 |
0.4 |
0.2 |
0.4 |
0.0 |
0.2 |
0.3 |
0.3 |
0.2 |
0.4 |
0.5 |
0.0 |
0.3 |
0.0 |
0.3 |
| Total |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
| n |
828.0 |
390.0 |
736.0 |
528.0 |
346.0 |
565.0 |
643.0 |
752.0 |
425.0 |
382.0 |
690.0 |
702.0 |
708.0 |
640.0 |
344.0 |
345.0 |
676.0 |
430.0 |
839.0 |
572.0 |
349.0 |
543.0 |
812.0 |
681.0 |
704.0 |
716.0 |
394.0 |
414.0 |
800.0 |
735.0 |
649.0 |
818.0 |
637.0 |
623.0 |
493.0 |
725.0 |
695.0 |
447.0 |
418.0 |
821.0 |
592.0 |
538.0 |
487.0 |
296.0 |
812.0 |
669.0 |
727.0 |
788.0 |
481.0 |
799.0 |
359.0 |
402.0 |
347.0 |
725.0 |
470.0 |
904.0 |
364.0 |
354.0 |
455.0 |
796.0 |
418.0 |
707.0 |
802.0 |
672.0 |
835.0 |
821.0 |
431.0 |
379.0 |
715.0 |
509.0 |
845.0 |
816.0 |
787.0 |
687.0 |
537.0 |
406.0 |
603.0 |
284.0 |
580.0 |
317.0 |
646.0 |
520.0 |
771.0 |
685.0 |
684.0 |
562.0 |
468.0 |
669.0 |
385.0 |
386.0 |
563.0 |
706.0 |
533.0 |
656.0 |
707.0 |
764.0 |
704.0 |
783.0 |
376.0 |
630.0 |
637.0 |
863.0 |
790.0 |
370.0 |
394.0 |
768.0 |
266.0 |
452.0 |
748.0 |
352.0 |
540.0 |
643.0 |
275.0 |
541.0 |
272.0 |
275.0 |
337.0 |
536.0 |
639.0 |
486.0 |
507.0 |
550.0 |
419.0 |
533.0 |
476.0 |
384.0 |
541.0 |
697.0 |
586.0 |
764.0 |
542.0 |
330.0 |
503.0 |
302.0 |
374.0 |
880.0 |
625.0 |
384.0 |
628.0 |
516.0 |
374.0 |
357.0 |
670.0 |
721.0 |
651.0 |
774.0 |
293.0 |
590.0 |
612.0 |
424.0 |
834.0 |
523.0 |
898.0 |
465.0 |
445.0 |
336.0 |
448.0 |
634.0 |
307.0 |
555.0 |
817.0 |
827.0 |
257.0 |
718.0 |
453.0 |
675.0 |
577.0 |
655.0 |
565.0 |
783.0 |
348.0 |
631.0 |
480.0 |
99151.0 |
| African American or Black |
2.35 |
0.35 |
1.09 |
0.39 |
0.49 |
-1.00 |
-2.49 |
0.37 |
0.38 |
0.36 |
0.30 |
1.27 |
0.36 |
-0.28 |
0.08 |
-1.42 |
0.81 |
0.56 |
1.53 |
-0.41 |
-1.19 |
-0.44 |
-0.90 |
0.64 |
1.55 |
-0.57 |
0.69 |
0.31 |
0.91 |
-0.21 |
-0.73 |
-0.01 |
-0.13 |
-0.47 |
-0.14 |
1.35 |
-0.92 |
0.38 |
0.24 |
0.24 |
-0.48 |
0.58 |
0.58 |
1.10 |
0.36 |
0.49 |
-0.41 |
0.28 |
-1.07 |
0.63 |
1.68 |
-1.39 |
-0.12 |
0.53 |
-0.52 |
0.14 |
-0.59 |
0.91 |
-1.18 |
-0.02 |
-0.30 |
-0.97 |
0.88 |
-1.89 |
-1.17 |
-0.72 |
0.14 |
-0.15 |
0.26 |
-0.03 |
0.51 |
2.14 |
-1.08 |
-0.29 |
-2.37 |
1.14 |
0.26 |
0.57 |
-0.07 |
-0.29 |
0.40 |
1.12 |
-0.28 |
0.47 |
-1.30 |
1.02 |
-1.12 |
1.02 |
0.16 |
-0.70 |
0.07 |
0.39 |
-0.88 |
-0.61 |
-1.08 |
0.80 |
-1.04 |
-0.63 |
-0.95 |
0.52 |
0.42 |
1.03 |
-0.43 |
0.02 |
-1.39 |
0.65 |
0.48 |
-0.35 |
0.92 |
0.22 |
1.50 |
-0.75 |
0.11 |
0.06 |
-1.49 |
-0.88 |
-0.68 |
-0.69 |
-0.37 |
0.72 |
-0.73 |
-0.43 |
0.49 |
1.50 |
0.39 |
-1.65 |
-0.06 |
1.87 |
0.52 |
0.60 |
0.05 |
-0.85 |
0.56 |
0.49 |
-0.91 |
1.18 |
-0.28 |
-0.24 |
-0.76 |
0.34 |
0.65 |
-0.90 |
-0.37 |
-0.13 |
2.05 |
1.16 |
1.17 |
1.25 |
0.57 |
0.40 |
-1.64 |
0.23 |
-1.71 |
-0.69 |
0.94 |
0.09 |
-2.10 |
-0.30 |
-0.09 |
0.08 |
-1.73 |
-3.47 |
-0.16 |
0.01 |
0.02 |
-0.44 |
-0.48 |
1.56 |
-0.42 |
-1.62 |
0.59 |
0.07 |
1.83 |
| American Indian or Alaska Native |
-0.28 |
-0.41 |
0.87 |
0.20 |
1.06 |
2.10 |
-0.67 |
-0.07 |
0.64 |
0.24 |
-1.25 |
-1.28 |
-1.29 |
0.29 |
-1.53 |
-0.24 |
-1.22 |
0.03 |
0.52 |
-1.47 |
-0.90 |
0.66 |
0.19 |
0.62 |
0.08 |
-0.41 |
2.62 |
1.29 |
-1.06 |
-1.35 |
0.74 |
-1.52 |
0.78 |
1.81 |
1.43 |
-0.88 |
-2.18 |
-0.61 |
0.08 |
0.58 |
1.46 |
0.69 |
-0.73 |
-0.02 |
-1.51 |
1.13 |
-1.78 |
0.69 |
0.94 |
-1.48 |
2.90 |
-1.66 |
0.40 |
-0.43 |
-0.68 |
0.33 |
0.95 |
-0.91 |
-0.07 |
0.24 |
0.08 |
0.98 |
-0.21 |
0.19 |
1.37 |
0.16 |
-0.55 |
-0.37 |
0.50 |
-0.80 |
-1.16 |
0.17 |
-0.17 |
0.14 |
-0.87 |
-0.47 |
-0.06 |
-0.68 |
-0.49 |
0.56 |
-1.15 |
0.23 |
-0.12 |
3.37 |
1.53 |
-0.94 |
-1.79 |
-0.27 |
1.46 |
-0.40 |
0.58 |
-0.38 |
1.23 |
-0.23 |
-0.38 |
-0.98 |
-1.28 |
-0.59 |
2.76 |
-1.11 |
1.26 |
0.86 |
-0.18 |
0.92 |
0.18 |
-0.11 |
-0.61 |
0.51 |
0.39 |
-0.26 |
-0.88 |
0.76 |
-0.64 |
0.15 |
-0.63 |
0.09 |
-0.86 |
-0.87 |
1.73 |
-0.18 |
-0.25 |
2.18 |
0.08 |
-0.34 |
-0.14 |
0.85 |
1.19 |
-1.27 |
-0.01 |
0.77 |
-0.89 |
1.16 |
1.92 |
1.34 |
0.27 |
0.40 |
0.35 |
-0.39 |
0.34 |
0.78 |
0.90 |
-0.28 |
-0.27 |
0.48 |
0.26 |
-0.13 |
-0.71 |
-0.02 |
-1.07 |
-0.53 |
0.54 |
-0.31 |
-0.06 |
0.46 |
0.55 |
-1.52 |
-0.61 |
0.32 |
-0.07 |
-0.41 |
1.02 |
-0.70 |
-0.57 |
0.04 |
0.51 |
-0.75 |
1.03 |
-0.23 |
-1.46 |
-0.59 |
-0.25 |
-1.60 |
-0.71 |
| Asian American |
0.28 |
-2.06 |
-1.74 |
-1.50 |
0.04 |
-0.79 |
0.44 |
-1.08 |
0.41 |
-0.28 |
0.77 |
-0.74 |
0.16 |
-0.25 |
-0.92 |
0.05 |
-2.17 |
-0.37 |
0.43 |
-2.25 |
-0.60 |
2.12 |
0.31 |
1.29 |
-2.14 |
-0.06 |
0.67 |
0.56 |
-0.87 |
-0.11 |
-0.92 |
0.38 |
1.23 |
0.52 |
0.04 |
-0.42 |
0.85 |
0.11 |
-0.21 |
1.63 |
1.48 |
-0.50 |
-0.22 |
-0.66 |
-0.72 |
0.01 |
0.38 |
-0.36 |
-0.82 |
1.35 |
1.01 |
-1.46 |
-0.76 |
-0.01 |
0.66 |
0.31 |
-0.41 |
0.31 |
0.52 |
-1.87 |
1.94 |
0.45 |
-0.24 |
-0.02 |
0.22 |
-0.16 |
-0.20 |
-0.80 |
0.23 |
0.65 |
-0.26 |
-0.88 |
0.17 |
-0.31 |
1.89 |
0.13 |
-0.15 |
-0.26 |
-0.50 |
0.07 |
-0.03 |
0.84 |
-0.33 |
-0.71 |
0.00 |
-0.14 |
1.37 |
0.72 |
0.05 |
-1.27 |
-0.93 |
0.73 |
-1.23 |
0.01 |
-0.10 |
0.00 |
0.89 |
-1.49 |
-0.01 |
-0.29 |
-0.51 |
-0.05 |
-0.90 |
0.65 |
-0.63 |
-0.44 |
-1.99 |
0.39 |
-1.31 |
0.34 |
0.27 |
0.44 |
0.99 |
0.73 |
0.60 |
-0.11 |
0.17 |
0.48 |
-0.53 |
0.79 |
-0.62 |
-1.72 |
-0.23 |
0.04 |
0.25 |
-1.24 |
1.36 |
0.14 |
1.25 |
0.13 |
0.88 |
0.68 |
1.71 |
-0.53 |
-1.31 |
1.03 |
-1.26 |
1.37 |
0.90 |
-0.08 |
1.15 |
0.07 |
-0.28 |
3.03 |
-0.80 |
0.17 |
-0.40 |
1.35 |
0.20 |
0.60 |
-0.15 |
1.60 |
0.37 |
-0.46 |
2.05 |
-1.21 |
0.45 |
-0.48 |
0.23 |
0.40 |
-0.38 |
-0.34 |
-0.72 |
-1.85 |
0.04 |
0.65 |
1.52 |
-1.27 |
-1.10 |
0.86 |
0.99 |
1.16 |
-0.64 |
| European American or white |
-0.78 |
0.59 |
-0.37 |
-0.13 |
-0.36 |
0.75 |
0.88 |
-0.69 |
-1.34 |
-0.71 |
-0.87 |
-0.01 |
0.75 |
-0.87 |
-0.28 |
0.19 |
1.20 |
0.21 |
-0.82 |
0.42 |
0.77 |
-0.60 |
-0.05 |
-0.68 |
-0.42 |
-0.29 |
-0.73 |
-0.02 |
0.31 |
0.97 |
0.65 |
-0.92 |
-0.51 |
-0.38 |
-0.21 |
-0.43 |
0.65 |
0.59 |
-0.09 |
-1.28 |
-1.48 |
-0.03 |
0.05 |
0.14 |
0.15 |
0.40 |
0.73 |
-0.44 |
0.99 |
-0.82 |
-0.93 |
1.51 |
0.11 |
0.54 |
-0.64 |
-0.41 |
-0.75 |
-0.38 |
0.31 |
0.76 |
-0.56 |
-0.61 |
0.60 |
1.15 |
-0.01 |
0.58 |
-0.48 |
0.95 |
-0.36 |
-1.58 |
0.07 |
0.04 |
0.36 |
0.20 |
0.83 |
-0.20 |
0.93 |
0.51 |
-0.34 |
-0.49 |
-0.01 |
-0.59 |
0.20 |
-0.21 |
-0.39 |
-0.89 |
0.50 |
-0.93 |
0.44 |
-0.37 |
0.41 |
0.04 |
1.67 |
0.34 |
0.99 |
-0.26 |
0.61 |
0.81 |
0.29 |
0.13 |
-0.73 |
0.22 |
1.35 |
-0.69 |
0.51 |
0.67 |
0.15 |
0.48 |
-0.49 |
0.50 |
-1.21 |
0.24 |
-0.66 |
-0.83 |
-0.11 |
0.91 |
-0.16 |
0.80 |
1.00 |
-0.29 |
-0.36 |
0.30 |
-0.60 |
-0.94 |
-0.46 |
1.10 |
-1.07 |
-0.18 |
-0.57 |
-1.15 |
-0.45 |
0.65 |
0.07 |
-0.11 |
1.85 |
-0.43 |
0.38 |
-0.02 |
0.29 |
-0.35 |
-1.05 |
0.23 |
0.84 |
-0.73 |
0.22 |
-0.28 |
0.91 |
-0.41 |
-0.67 |
-0.44 |
0.82 |
0.69 |
-0.13 |
-0.29 |
-0.05 |
-0.27 |
-0.16 |
-0.21 |
1.09 |
-0.91 |
0.78 |
1.67 |
0.65 |
1.19 |
0.19 |
0.38 |
-0.13 |
-0.48 |
0.35 |
-0.27 |
0.00 |
-1.21 |
-0.84 |
| Latino/a/x American |
-0.76 |
0.33 |
0.50 |
0.51 |
-0.34 |
-0.22 |
0.39 |
0.55 |
1.78 |
0.19 |
1.31 |
-0.23 |
-0.50 |
1.31 |
0.66 |
0.40 |
-0.71 |
-0.98 |
-1.23 |
1.58 |
0.65 |
-0.73 |
0.95 |
-0.20 |
-0.35 |
0.68 |
-0.42 |
-0.54 |
-0.01 |
-0.78 |
0.45 |
1.08 |
0.24 |
0.14 |
-0.33 |
0.44 |
-0.19 |
-0.50 |
-0.94 |
0.88 |
1.54 |
-0.16 |
-0.41 |
0.45 |
-0.66 |
-0.84 |
-0.49 |
1.51 |
-1.09 |
0.39 |
-0.18 |
-0.49 |
0.70 |
-0.70 |
1.46 |
0.38 |
1.66 |
-0.29 |
-0.36 |
-0.26 |
-0.73 |
0.26 |
-1.59 |
-0.81 |
0.58 |
-0.11 |
1.00 |
-0.53 |
0.45 |
1.97 |
-0.64 |
-1.26 |
-0.27 |
0.11 |
-0.14 |
-0.91 |
-1.54 |
-1.30 |
0.42 |
1.25 |
-0.10 |
-0.09 |
-0.48 |
-0.44 |
1.17 |
0.30 |
-0.02 |
0.26 |
-1.34 |
1.88 |
-0.08 |
-0.79 |
-0.64 |
0.24 |
-0.32 |
0.27 |
-0.35 |
-0.05 |
-0.80 |
0.02 |
0.41 |
-1.14 |
-1.57 |
-0.32 |
0.24 |
-1.07 |
0.78 |
-1.02 |
0.94 |
-1.41 |
0.74 |
-0.13 |
0.38 |
1.29 |
1.00 |
-0.54 |
0.84 |
-0.60 |
-1.10 |
-0.49 |
0.17 |
0.26 |
-0.32 |
0.22 |
0.32 |
0.25 |
0.34 |
-0.48 |
-0.43 |
2.33 |
0.04 |
-0.18 |
-1.90 |
-0.22 |
-1.21 |
-0.57 |
-0.42 |
-0.08 |
-0.21 |
0.08 |
0.04 |
0.45 |
-0.69 |
-0.88 |
-1.31 |
0.02 |
-1.77 |
-0.41 |
0.70 |
0.31 |
0.21 |
-1.21 |
1.58 |
0.66 |
-2.02 |
1.34 |
1.24 |
0.64 |
-0.85 |
1.65 |
-0.13 |
-0.06 |
0.36 |
0.16 |
-0.52 |
-0.52 |
0.38 |
0.68 |
0.43 |
1.13 |
-0.15 |
1.31 |
0.03 |
| Multiracial |
0.11 |
-1.04 |
0.27 |
0.45 |
0.70 |
0.12 |
0.45 |
1.94 |
-0.63 |
1.55 |
-0.86 |
-0.39 |
-1.91 |
0.67 |
0.99 |
1.24 |
-0.57 |
0.99 |
2.07 |
-0.56 |
-0.91 |
0.94 |
-0.98 |
-0.42 |
2.18 |
0.97 |
0.41 |
-1.01 |
-0.90 |
-0.45 |
-0.95 |
0.87 |
-0.28 |
0.43 |
1.45 |
-0.92 |
-0.34 |
-1.52 |
2.32 |
-0.01 |
0.49 |
-0.28 |
0.40 |
-2.36 |
1.61 |
-0.71 |
-0.94 |
-1.87 |
1.80 |
-0.54 |
-1.53 |
0.82 |
-1.16 |
-0.74 |
-1.02 |
-0.57 |
0.23 |
0.34 |
0.72 |
0.17 |
1.36 |
2.34 |
0.30 |
1.16 |
-0.62 |
-0.87 |
-0.44 |
-0.69 |
-0.30 |
0.63 |
1.34 |
0.20 |
0.94 |
-0.09 |
-0.27 |
0.77 |
-0.20 |
0.94 |
0.80 |
-0.59 |
0.23 |
-0.55 |
1.42 |
-0.08 |
0.68 |
1.39 |
-1.00 |
0.05 |
0.76 |
-0.25 |
-0.49 |
0.32 |
-1.72 |
-0.61 |
-0.80 |
-0.48 |
-0.04 |
0.44 |
1.37 |
-0.61 |
1.28 |
-0.47 |
0.91 |
2.21 |
0.91 |
-0.68 |
-0.01 |
0.75 |
-0.36 |
0.63 |
-0.29 |
-0.32 |
0.18 |
-0.94 |
-0.08 |
-0.42 |
0.01 |
-0.47 |
-0.49 |
0.19 |
2.18 |
0.88 |
2.07 |
0.40 |
0.28 |
-0.23 |
0.75 |
-1.28 |
0.34 |
-2.43 |
0.74 |
-1.54 |
-0.19 |
-0.15 |
-0.38 |
-0.91 |
1.19 |
-0.73 |
-0.60 |
0.35 |
0.12 |
-0.73 |
-0.15 |
0.57 |
-0.19 |
-1.25 |
-0.33 |
-1.91 |
-0.08 |
-0.85 |
-0.44 |
-1.87 |
-0.20 |
1.07 |
0.13 |
0.02 |
1.03 |
0.14 |
-1.89 |
-0.84 |
0.54 |
1.14 |
-1.73 |
-1.79 |
0.28 |
0.00 |
-2.03 |
-0.61 |
0.53 |
0.09 |
-1.69 |
0.36 |
1.14 |
| Pacific Islander |
-0.25 |
1.77 |
-0.08 |
0.39 |
0.00 |
-1.28 |
-1.36 |
0.56 |
-0.20 |
0.86 |
0.01 |
0.69 |
-0.03 |
0.85 |
1.01 |
-1.00 |
-0.68 |
-0.22 |
0.37 |
-0.51 |
-0.01 |
1.94 |
0.43 |
0.74 |
0.68 |
-0.74 |
-0.13 |
1.65 |
-0.86 |
-0.77 |
-0.64 |
0.42 |
-0.62 |
0.15 |
-1.19 |
-0.06 |
-0.71 |
-0.25 |
-0.19 |
-0.89 |
-1.31 |
0.36 |
-0.34 |
-0.92 |
1.08 |
-0.67 |
1.31 |
-0.84 |
-1.18 |
1.12 |
-1.02 |
-0.15 |
1.00 |
-1.45 |
1.41 |
1.48 |
-0.05 |
-0.02 |
1.47 |
0.46 |
0.72 |
-0.03 |
-0.21 |
-0.67 |
2.31 |
1.71 |
0.68 |
-0.09 |
-1.44 |
0.44 |
-0.92 |
-0.88 |
0.48 |
0.01 |
-1.24 |
-0.16 |
0.20 |
-0.91 |
1.80 |
-0.96 |
-0.63 |
-1.22 |
-0.15 |
2.15 |
-0.69 |
-0.49 |
0.56 |
0.05 |
-1.05 |
1.79 |
0.30 |
-0.73 |
-0.43 |
0.08 |
0.67 |
-0.81 |
-0.72 |
0.49 |
-0.08 |
0.14 |
-0.62 |
0.32 |
-0.85 |
-1.03 |
-0.13 |
0.53 |
-0.88 |
-1.14 |
-0.11 |
-0.02 |
1.16 |
0.84 |
0.23 |
-0.45 |
1.37 |
0.23 |
0.03 |
-0.44 |
0.12 |
-1.18 |
3.75 |
-1.26 |
0.72 |
-0.43 |
-1.17 |
-0.10 |
0.35 |
-0.01 |
0.24 |
-0.81 |
-0.45 |
-0.98 |
0.46 |
1.21 |
-1.04 |
0.29 |
1.64 |
-1.05 |
0.14 |
-0.40 |
1.85 |
1.94 |
-0.67 |
-0.06 |
-1.37 |
-0.16 |
-0.92 |
1.00 |
0.18 |
1.61 |
-0.26 |
-1.23 |
-0.99 |
0.57 |
-0.25 |
0.03 |
0.62 |
0.87 |
-0.94 |
-0.47 |
0.42 |
-0.25 |
0.30 |
-1.44 |
-0.27 |
0.04 |
0.26 |
-0.65 |
0.29 |
1.16 |
-1.00 |
0.13 |
-1.18 |
X-squared = 975.0584, df = 1032, p = 0.8966
correlation_table(majors_demographics$ethnoracial_group, majors_demographics$division)
| African American or Black |
1171 |
1194 |
816 |
2961 |
1682 |
5275 |
| American Indian or Alaska Native |
78 |
64 |
35 |
139 |
87 |
276 |
| Asian American |
674 |
662 |
472 |
1638 |
992 |
2959 |
| European American or white |
4770 |
4705 |
3138 |
11782 |
7268 |
21325 |
| Latino/a/x American |
1831 |
1837 |
1247 |
4555 |
2736 |
8384 |
| Multiracial |
371 |
320 |
237 |
879 |
592 |
1713 |
| Pacific Islander |
34 |
18 |
16 |
60 |
36 |
122 |
| African American or Black |
8.9 |
9.1 |
6.2 |
22.6 |
12.8 |
40.3 |
100 |
13099 |
| American Indian or Alaska Native |
11.5 |
9.4 |
5.2 |
20.5 |
12.8 |
40.6 |
100 |
679 |
| Asian American |
9.1 |
8.9 |
6.4 |
22.1 |
13.4 |
40.0 |
100 |
7397 |
| European American or white |
9.0 |
8.9 |
5.9 |
22.2 |
13.7 |
40.2 |
100 |
52988 |
| Latino/a/x American |
8.9 |
8.9 |
6.1 |
22.1 |
13.3 |
40.7 |
100 |
20590 |
| Multiracial |
9.0 |
7.8 |
5.8 |
21.4 |
14.4 |
41.7 |
100 |
4112 |
| Pacific Islander |
11.9 |
6.3 |
5.6 |
21.0 |
12.6 |
42.7 |
100 |
286 |
| All |
9.0 |
8.9 |
6.0 |
22.2 |
13.5 |
40.4 |
100 |
99151 |
| African American or Black |
13.1 |
13.6 |
13.7 |
13.5 |
12.6 |
13.2 |
13.2 |
| American Indian or Alaska Native |
0.9 |
0.7 |
0.6 |
0.6 |
0.6 |
0.7 |
0.7 |
| Asian American |
7.5 |
7.5 |
7.9 |
7.4 |
7.4 |
7.4 |
7.5 |
| European American or white |
53.4 |
53.5 |
52.6 |
53.5 |
54.3 |
53.2 |
53.4 |
| Latino/a/x American |
20.5 |
20.9 |
20.9 |
20.7 |
20.4 |
20.9 |
20.8 |
| Multiracial |
4.2 |
3.6 |
4.0 |
4.0 |
4.4 |
4.3 |
4.1 |
| Pacific Islander |
0.4 |
0.2 |
0.3 |
0.3 |
0.3 |
0.3 |
0.3 |
| Total |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
| n |
8929.0 |
8800.0 |
5961.0 |
22014.0 |
13393.0 |
40054.0 |
99151.0 |
| African American or Black |
-0.25 |
0.92 |
1.01 |
0.98 |
-2.08 |
-0.23 |
| American Indian or Alaska Native |
2.16 |
0.48 |
-0.91 |
-0.96 |
-0.49 |
0.10 |
| Asian American |
0.30 |
0.21 |
1.29 |
-0.11 |
-0.23 |
-0.53 |
| European American or white |
-0.03 |
0.03 |
-0.84 |
0.16 |
1.31 |
-0.55 |
| Latino/a/x American |
-0.54 |
0.22 |
0.26 |
-0.24 |
-0.86 |
0.73 |
| Multiracial |
0.04 |
-2.35 |
-0.65 |
-1.12 |
1.55 |
1.27 |
| Pacific Islander |
1.62 |
-1.47 |
-0.29 |
-0.44 |
-0.42 |
0.60 |
X-squared = 37.638, df = 30, p = 0.1593
International Status
We examine major along whether a student is an international or a
domestic student. The insignificant p-value means that there was no
clear pattern between international student status and major. Thus,
international student status did not impact the algorithm for major
choice.
correlation_table(majors_demographics$international_status, majors_demographics$major)
| Domestic |
787 |
368 |
691 |
502 |
324 |
532 |
593 |
703 |
406 |
359 |
659 |
666 |
662 |
598 |
323 |
326 |
640 |
411 |
784 |
545 |
332 |
513 |
764 |
644 |
661 |
680 |
364 |
390 |
753 |
703 |
602 |
766 |
610 |
587 |
465 |
693 |
652 |
421 |
391 |
773 |
556 |
511 |
461 |
285 |
761 |
624 |
696 |
750 |
456 |
751 |
333 |
384 |
324 |
681 |
441 |
849 |
331 |
330 |
427 |
760 |
392 |
661 |
754 |
630 |
785 |
762 |
410 |
355 |
684 |
478 |
784 |
764 |
750 |
648 |
510 |
380 |
569 |
268 |
545 |
303 |
612 |
494 |
724 |
632 |
637 |
532 |
448 |
630 |
359 |
366 |
526 |
665 |
509 |
620 |
663 |
715 |
659 |
738 |
348 |
595 |
602 |
814 |
731 |
354 |
374 |
718 |
241 |
432 |
707 |
331 |
514 |
597 |
261 |
510 |
251 |
263 |
312 |
508 |
592 |
462 |
480 |
524 |
392 |
498 |
446 |
351 |
506 |
648 |
557 |
719 |
505 |
322 |
474 |
286 |
351 |
825 |
584 |
358 |
587 |
484 |
355 |
331 |
625 |
680 |
603 |
736 |
275 |
560 |
579 |
393 |
786 |
499 |
849 |
434 |
419 |
323 |
421 |
602 |
286 |
530 |
759 |
780 |
243 |
671 |
431 |
635 |
546 |
615 |
529 |
736 |
336 |
591 |
451 |
| International |
41 |
22 |
45 |
26 |
22 |
33 |
50 |
49 |
19 |
23 |
31 |
36 |
46 |
42 |
21 |
19 |
36 |
19 |
55 |
27 |
17 |
30 |
48 |
37 |
43 |
36 |
30 |
24 |
47 |
32 |
47 |
52 |
27 |
36 |
28 |
32 |
43 |
26 |
27 |
48 |
36 |
27 |
26 |
11 |
51 |
45 |
31 |
38 |
25 |
48 |
26 |
18 |
23 |
44 |
29 |
55 |
33 |
24 |
28 |
36 |
26 |
46 |
48 |
42 |
50 |
59 |
21 |
24 |
31 |
31 |
61 |
52 |
37 |
39 |
27 |
26 |
34 |
16 |
35 |
14 |
34 |
26 |
47 |
53 |
47 |
30 |
20 |
39 |
26 |
20 |
37 |
41 |
24 |
36 |
44 |
49 |
45 |
45 |
28 |
35 |
35 |
49 |
59 |
16 |
20 |
50 |
25 |
20 |
41 |
21 |
26 |
46 |
14 |
31 |
21 |
12 |
25 |
28 |
47 |
24 |
27 |
26 |
27 |
35 |
30 |
33 |
35 |
49 |
29 |
45 |
37 |
8 |
29 |
16 |
23 |
55 |
41 |
26 |
41 |
32 |
19 |
26 |
45 |
41 |
48 |
38 |
18 |
30 |
33 |
31 |
48 |
24 |
49 |
31 |
26 |
13 |
27 |
32 |
21 |
25 |
58 |
47 |
14 |
47 |
22 |
40 |
31 |
40 |
36 |
47 |
12 |
40 |
29 |
| Domestic |
0.8 |
0.4 |
0.7 |
0.5 |
0.3 |
0.6 |
0.6 |
0.8 |
0.4 |
0.4 |
0.7 |
0.7 |
0.7 |
0.6 |
0.3 |
0.3 |
0.7 |
0.4 |
0.8 |
0.6 |
0.4 |
0.5 |
0.8 |
0.7 |
0.7 |
0.7 |
0.4 |
0.4 |
0.8 |
0.8 |
0.6 |
0.8 |
0.7 |
0.6 |
0.5 |
0.7 |
0.7 |
0.5 |
0.4 |
0.8 |
0.6 |
0.5 |
0.5 |
0.3 |
0.8 |
0.7 |
0.7 |
0.8 |
0.5 |
0.8 |
0.4 |
0.4 |
0.3 |
0.7 |
0.5 |
0.9 |
0.4 |
0.4 |
0.5 |
0.8 |
0.4 |
0.7 |
0.8 |
0.7 |
0.8 |
0.8 |
0.4 |
0.4 |
0.7 |
0.5 |
0.8 |
0.8 |
0.8 |
0.7 |
0.5 |
0.4 |
0.6 |
0.3 |
0.6 |
0.3 |
0.7 |
0.5 |
0.8 |
0.7 |
0.7 |
0.6 |
0.5 |
0.7 |
0.4 |
0.4 |
0.6 |
0.7 |
0.5 |
0.7 |
0.7 |
0.8 |
0.7 |
0.8 |
0.4 |
0.6 |
0.6 |
0.9 |
0.8 |
0.4 |
0.4 |
0.8 |
0.3 |
0.5 |
0.8 |
0.4 |
0.6 |
0.6 |
0.3 |
0.5 |
0.3 |
0.3 |
0.3 |
0.5 |
0.6 |
0.5 |
0.5 |
0.6 |
0.4 |
0.5 |
0.5 |
0.4 |
0.5 |
0.7 |
0.6 |
0.8 |
0.5 |
0.3 |
0.5 |
0.3 |
0.4 |
0.9 |
0.6 |
0.4 |
0.6 |
0.5 |
0.4 |
0.4 |
0.7 |
0.7 |
0.6 |
0.8 |
0.3 |
0.6 |
0.6 |
0.4 |
0.8 |
0.5 |
0.9 |
0.5 |
0.4 |
0.3 |
0.5 |
0.6 |
0.3 |
0.6 |
0.8 |
0.8 |
0.3 |
0.7 |
0.5 |
0.7 |
0.6 |
0.7 |
0.6 |
0.8 |
0.4 |
0.6 |
0.5 |
100 |
93338 |
| International |
0.7 |
0.4 |
0.8 |
0.4 |
0.4 |
0.6 |
0.9 |
0.8 |
0.3 |
0.4 |
0.5 |
0.6 |
0.8 |
0.7 |
0.4 |
0.3 |
0.6 |
0.3 |
0.9 |
0.5 |
0.3 |
0.5 |
0.8 |
0.6 |
0.7 |
0.6 |
0.5 |
0.4 |
0.8 |
0.6 |
0.8 |
0.9 |
0.5 |
0.6 |
0.5 |
0.6 |
0.7 |
0.4 |
0.5 |
0.8 |
0.6 |
0.5 |
0.4 |
0.2 |
0.9 |
0.8 |
0.5 |
0.7 |
0.4 |
0.8 |
0.4 |
0.3 |
0.4 |
0.8 |
0.5 |
0.9 |
0.6 |
0.4 |
0.5 |
0.6 |
0.4 |
0.8 |
0.8 |
0.7 |
0.9 |
1.0 |
0.4 |
0.4 |
0.5 |
0.5 |
1.0 |
0.9 |
0.6 |
0.7 |
0.5 |
0.4 |
0.6 |
0.3 |
0.6 |
0.2 |
0.6 |
0.4 |
0.8 |
0.9 |
0.8 |
0.5 |
0.3 |
0.7 |
0.4 |
0.3 |
0.6 |
0.7 |
0.4 |
0.6 |
0.8 |
0.8 |
0.8 |
0.8 |
0.5 |
0.6 |
0.6 |
0.8 |
1.0 |
0.3 |
0.3 |
0.9 |
0.4 |
0.3 |
0.7 |
0.4 |
0.4 |
0.8 |
0.2 |
0.5 |
0.4 |
0.2 |
0.4 |
0.5 |
0.8 |
0.4 |
0.5 |
0.4 |
0.5 |
0.6 |
0.5 |
0.6 |
0.6 |
0.8 |
0.5 |
0.8 |
0.6 |
0.1 |
0.5 |
0.3 |
0.4 |
0.9 |
0.7 |
0.4 |
0.7 |
0.6 |
0.3 |
0.4 |
0.8 |
0.7 |
0.8 |
0.7 |
0.3 |
0.5 |
0.6 |
0.5 |
0.8 |
0.4 |
0.8 |
0.5 |
0.4 |
0.2 |
0.5 |
0.6 |
0.4 |
0.4 |
1.0 |
0.8 |
0.2 |
0.8 |
0.4 |
0.7 |
0.5 |
0.7 |
0.6 |
0.8 |
0.2 |
0.7 |
0.5 |
100 |
5813 |
| All |
0.8 |
0.4 |
0.7 |
0.5 |
0.3 |
0.6 |
0.6 |
0.8 |
0.4 |
0.4 |
0.7 |
0.7 |
0.7 |
0.6 |
0.3 |
0.3 |
0.7 |
0.4 |
0.8 |
0.6 |
0.4 |
0.5 |
0.8 |
0.7 |
0.7 |
0.7 |
0.4 |
0.4 |
0.8 |
0.7 |
0.7 |
0.8 |
0.6 |
0.6 |
0.5 |
0.7 |
0.7 |
0.5 |
0.4 |
0.8 |
0.6 |
0.5 |
0.5 |
0.3 |
0.8 |
0.7 |
0.7 |
0.8 |
0.5 |
0.8 |
0.4 |
0.4 |
0.3 |
0.7 |
0.5 |
0.9 |
0.4 |
0.4 |
0.5 |
0.8 |
0.4 |
0.7 |
0.8 |
0.7 |
0.8 |
0.8 |
0.4 |
0.4 |
0.7 |
0.5 |
0.9 |
0.8 |
0.8 |
0.7 |
0.5 |
0.4 |
0.6 |
0.3 |
0.6 |
0.3 |
0.7 |
0.5 |
0.8 |
0.7 |
0.7 |
0.6 |
0.5 |
0.7 |
0.4 |
0.4 |
0.6 |
0.7 |
0.5 |
0.7 |
0.7 |
0.8 |
0.7 |
0.8 |
0.4 |
0.6 |
0.6 |
0.9 |
0.8 |
0.4 |
0.4 |
0.8 |
0.3 |
0.5 |
0.8 |
0.4 |
0.5 |
0.6 |
0.3 |
0.5 |
0.3 |
0.3 |
0.3 |
0.5 |
0.6 |
0.5 |
0.5 |
0.6 |
0.4 |
0.5 |
0.5 |
0.4 |
0.5 |
0.7 |
0.6 |
0.8 |
0.5 |
0.3 |
0.5 |
0.3 |
0.4 |
0.9 |
0.6 |
0.4 |
0.6 |
0.5 |
0.4 |
0.4 |
0.7 |
0.7 |
0.7 |
0.8 |
0.3 |
0.6 |
0.6 |
0.4 |
0.8 |
0.5 |
0.9 |
0.5 |
0.4 |
0.3 |
0.5 |
0.6 |
0.3 |
0.6 |
0.8 |
0.8 |
0.3 |
0.7 |
0.5 |
0.7 |
0.6 |
0.7 |
0.6 |
0.8 |
0.4 |
0.6 |
0.5 |
100 |
99151 |
| Domestic |
95 |
94.4 |
93.9 |
95.1 |
93.6 |
94.2 |
92.2 |
93.5 |
95.5 |
94 |
95.5 |
94.9 |
93.5 |
93.4 |
93.9 |
94.5 |
94.7 |
95.6 |
93.4 |
95.3 |
95.1 |
94.5 |
94.1 |
94.6 |
93.9 |
95 |
92.4 |
94.2 |
94.1 |
95.6 |
92.8 |
93.6 |
95.8 |
94.2 |
94.3 |
95.6 |
93.8 |
94.2 |
93.5 |
94.2 |
93.9 |
95 |
94.7 |
96.3 |
93.7 |
93.3 |
95.7 |
95.2 |
94.8 |
94 |
92.8 |
95.5 |
93.4 |
93.9 |
93.8 |
93.9 |
90.9 |
93.2 |
93.8 |
95.5 |
93.8 |
93.5 |
94 |
93.8 |
94 |
92.8 |
95.1 |
93.7 |
95.7 |
93.9 |
92.8 |
93.6 |
95.3 |
94.3 |
95 |
93.6 |
94.4 |
94.4 |
94 |
95.6 |
94.7 |
95 |
93.9 |
92.3 |
93.1 |
94.7 |
95.7 |
94.2 |
93.2 |
94.8 |
93.4 |
94.2 |
95.5 |
94.5 |
93.8 |
93.6 |
93.6 |
94.3 |
92.6 |
94.4 |
94.5 |
94.3 |
92.5 |
95.7 |
94.9 |
93.5 |
90.6 |
95.6 |
94.5 |
94 |
95.2 |
92.8 |
94.9 |
94.3 |
92.3 |
95.6 |
92.6 |
94.8 |
92.6 |
95.1 |
94.7 |
95.3 |
93.6 |
93.4 |
93.7 |
91.4 |
93.5 |
93 |
95.1 |
94.1 |
93.2 |
97.6 |
94.2 |
94.7 |
93.9 |
93.8 |
93.4 |
93.2 |
93.5 |
93.8 |
94.9 |
92.7 |
93.3 |
94.3 |
92.6 |
95.1 |
93.9 |
94.9 |
94.6 |
92.7 |
94.2 |
95.4 |
94.5 |
93.3 |
94.2 |
96.1 |
94 |
95 |
93.2 |
95.5 |
92.9 |
94.3 |
94.6 |
93.5 |
95.1 |
94.1 |
94.6 |
93.9 |
93.6 |
94 |
96.6 |
93.7 |
94 |
94.1 |
| International |
5 |
5.6 |
6.1 |
4.9 |
6.4 |
5.8 |
7.8 |
6.5 |
4.5 |
6 |
4.5 |
5.1 |
6.5 |
6.6 |
6.1 |
5.5 |
5.3 |
4.4 |
6.6 |
4.7 |
4.9 |
5.5 |
5.9 |
5.4 |
6.1 |
5 |
7.6 |
5.8 |
5.9 |
4.4 |
7.2 |
6.4 |
4.2 |
5.8 |
5.7 |
4.4 |
6.2 |
5.8 |
6.5 |
5.8 |
6.1 |
5 |
5.3 |
3.7 |
6.3 |
6.7 |
4.3 |
4.8 |
5.2 |
6 |
7.2 |
4.5 |
6.6 |
6.1 |
6.2 |
6.1 |
9.1 |
6.8 |
6.2 |
4.5 |
6.2 |
6.5 |
6 |
6.2 |
6 |
7.2 |
4.9 |
6.3 |
4.3 |
6.1 |
7.2 |
6.4 |
4.7 |
5.7 |
5 |
6.4 |
5.6 |
5.6 |
6 |
4.4 |
5.3 |
5 |
6.1 |
7.7 |
6.9 |
5.3 |
4.3 |
5.8 |
6.8 |
5.2 |
6.6 |
5.8 |
4.5 |
5.5 |
6.2 |
6.4 |
6.4 |
5.7 |
7.4 |
5.6 |
5.5 |
5.7 |
7.5 |
4.3 |
5.1 |
6.5 |
9.4 |
4.4 |
5.5 |
6 |
4.8 |
7.2 |
5.1 |
5.7 |
7.7 |
4.4 |
7.4 |
5.2 |
7.4 |
4.9 |
5.3 |
4.7 |
6.4 |
6.6 |
6.3 |
8.6 |
6.5 |
7 |
4.9 |
5.9 |
6.8 |
2.4 |
5.8 |
5.3 |
6.1 |
6.2 |
6.6 |
6.8 |
6.5 |
6.2 |
5.1 |
7.3 |
6.7 |
5.7 |
7.4 |
4.9 |
6.1 |
5.1 |
5.4 |
7.3 |
5.8 |
4.6 |
5.5 |
6.7 |
5.8 |
3.9 |
6 |
5 |
6.8 |
4.5 |
7.1 |
5.7 |
5.4 |
6.5 |
4.9 |
5.9 |
5.4 |
6.1 |
6.4 |
6 |
3.4 |
6.3 |
6 |
5.9 |
| Total |
100 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100 |
100.0 |
100 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100 |
100.0 |
100.0 |
100.0 |
100 |
100.0 |
100.0 |
100 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100 |
100 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100 |
100.0 |
100.0 |
100 |
100.0 |
| n |
828 |
390.0 |
736.0 |
528.0 |
346.0 |
565.0 |
643.0 |
752.0 |
425.0 |
382 |
690.0 |
702.0 |
708.0 |
640.0 |
344.0 |
345.0 |
676.0 |
430.0 |
839.0 |
572.0 |
349.0 |
543.0 |
812.0 |
681.0 |
704.0 |
716 |
394.0 |
414.0 |
800.0 |
735.0 |
649.0 |
818.0 |
637.0 |
623.0 |
493.0 |
725.0 |
695.0 |
447.0 |
418.0 |
821.0 |
592.0 |
538 |
487.0 |
296.0 |
812.0 |
669.0 |
727.0 |
788.0 |
481.0 |
799 |
359.0 |
402.0 |
347.0 |
725.0 |
470.0 |
904.0 |
364.0 |
354.0 |
455.0 |
796.0 |
418.0 |
707.0 |
802 |
672.0 |
835 |
821.0 |
431.0 |
379.0 |
715.0 |
509.0 |
845.0 |
816.0 |
787.0 |
687.0 |
537 |
406.0 |
603.0 |
284.0 |
580 |
317.0 |
646.0 |
520 |
771.0 |
685.0 |
684.0 |
562.0 |
468.0 |
669.0 |
385.0 |
386.0 |
563.0 |
706.0 |
533.0 |
656.0 |
707.0 |
764.0 |
704.0 |
783.0 |
376.0 |
630.0 |
637.0 |
863.0 |
790.0 |
370.0 |
394.0 |
768.0 |
266.0 |
452.0 |
748.0 |
352 |
540.0 |
643.0 |
275.0 |
541.0 |
272.0 |
275.0 |
337.0 |
536.0 |
639.0 |
486.0 |
507.0 |
550.0 |
419.0 |
533.0 |
476.0 |
384.0 |
541.0 |
697 |
586.0 |
764.0 |
542.0 |
330.0 |
503.0 |
302.0 |
374.0 |
880.0 |
625.0 |
384.0 |
628.0 |
516.0 |
374.0 |
357.0 |
670.0 |
721.0 |
651.0 |
774.0 |
293.0 |
590.0 |
612.0 |
424.0 |
834.0 |
523.0 |
898.0 |
465.0 |
445.0 |
336.0 |
448 |
634 |
307.0 |
555.0 |
817.0 |
827.0 |
257.0 |
718.0 |
453.0 |
675.0 |
577.0 |
655.0 |
565.0 |
783 |
348.0 |
631.0 |
480 |
99151.0 |
| Domestic |
0.27 |
0.05 |
-0.07 |
0.22 |
-0.10 |
0.01 |
-0.5 |
-0.18 |
0.30 |
-0.03 |
0.37 |
0.2 |
-0.17 |
-0.18 |
-0.05 |
0.07 |
0.14 |
0.31 |
-0.21 |
0.28 |
0.19 |
0.08 |
-0.01 |
0.12 |
-0.07 |
0.23 |
-0.36 |
0.01 |
0.00 |
0.42 |
-0.36 |
-0.15 |
0.42 |
0.02 |
0.04 |
0.40 |
-0.09 |
0.01 |
-0.13 |
0.00 |
-0.05 |
0.20 |
0.12 |
0.38 |
-0.12 |
-0.23 |
0.44 |
0.30 |
0.15 |
-0.04 |
-0.27 |
0.29 |
-0.15 |
-0.06 |
-0.07 |
-0.07 |
-0.63 |
-0.18 |
-0.06 |
0.39 |
-0.08 |
-0.18 |
-0.04 |
-0.10 |
-0.04 |
-0.39 |
0.21 |
-0.09 |
0.42 |
-0.05 |
-0.41 |
-0.15 |
0.34 |
0.05 |
0.2 |
-0.11 |
0.06 |
0.04 |
-0.04 |
0.27 |
0.16 |
0.20 |
-0.07 |
-0.51 |
-0.27 |
0.13 |
0.35 |
0.01 |
-0.18 |
0.14 |
-0.17 |
0.02 |
0.32 |
0.1 |
-0.1 |
-0.16 |
-0.14 |
0.03 |
-0.32 |
0.08 |
0.10 |
0.06 |
-0.47 |
0.31 |
0.16 |
-0.18 |
-0.59 |
0.32 |
0.11 |
-0.02 |
0.25 |
-0.34 |
0.13 |
0.03 |
-0.32 |
0.26 |
-0.29 |
0.15 |
-0.39 |
0.21 |
0.12 |
0.27 |
-0.12 |
-0.17 |
-0.1 |
-0.55 |
-0.15 |
-0.32 |
0.23 |
-0.01 |
-0.23 |
0.64 |
0.02 |
0.10 |
-0.06 |
-0.12 |
-0.18 |
-0.18 |
-0.17 |
-0.08 |
0.16 |
-0.28 |
-0.23 |
0.05 |
-0.40 |
0.27 |
-0.05 |
0.19 |
0.12 |
-0.31 |
0.03 |
0.3 |
0.13 |
-0.18 |
0.00 |
0.38 |
-0.04 |
0.21 |
-0.18 |
0.33 |
-0.36 |
0.05 |
0.07 |
-0.19 |
0.22 |
-0.02 |
0.12 |
-0.06 |
-0.12 |
-0.04 |
0.46 |
-0.12 |
-0.04 |
| International |
-1.08 |
-0.18 |
0.28 |
-0.89 |
0.38 |
-0.02 |
2.0 |
0.74 |
-1.19 |
0.13 |
-1.49 |
-0.8 |
0.70 |
0.73 |
0.19 |
-0.27 |
-0.58 |
-1.24 |
0.83 |
-1.13 |
-0.77 |
-0.33 |
0.06 |
-0.46 |
0.27 |
-0.92 |
1.44 |
-0.06 |
0.01 |
-1.69 |
1.45 |
0.58 |
-1.69 |
-0.09 |
-0.17 |
-1.61 |
0.35 |
-0.04 |
0.50 |
-0.02 |
0.22 |
-0.81 |
-0.48 |
-1.53 |
0.49 |
0.92 |
-1.78 |
-1.21 |
-0.60 |
0.17 |
1.08 |
-1.15 |
0.59 |
0.23 |
0.28 |
0.27 |
2.52 |
0.71 |
0.26 |
-1.56 |
0.30 |
0.71 |
0.14 |
0.41 |
0.15 |
1.57 |
-0.85 |
0.38 |
-1.69 |
0.21 |
1.63 |
0.60 |
-1.35 |
-0.20 |
-0.8 |
0.45 |
-0.23 |
-0.16 |
0.17 |
-1.06 |
-0.63 |
-0.81 |
0.27 |
2.03 |
1.09 |
-0.51 |
-1.42 |
-0.04 |
0.72 |
-0.55 |
0.69 |
-0.06 |
-1.30 |
-0.4 |
0.4 |
0.63 |
0.58 |
-0.13 |
1.27 |
-0.32 |
-0.38 |
-0.22 |
1.86 |
-1.22 |
-0.64 |
0.74 |
2.38 |
-1.26 |
-0.43 |
0.08 |
-1.01 |
1.35 |
-0.53 |
-0.13 |
1.27 |
-1.03 |
1.18 |
-0.61 |
1.56 |
-0.84 |
-0.50 |
-1.10 |
0.49 |
0.67 |
0.4 |
2.21 |
0.58 |
1.27 |
-0.91 |
0.03 |
0.93 |
-2.58 |
-0.09 |
-0.41 |
0.23 |
0.47 |
0.72 |
0.73 |
0.69 |
0.32 |
-0.63 |
1.11 |
0.91 |
-0.20 |
1.59 |
-1.10 |
0.20 |
-0.78 |
-0.48 |
1.23 |
-0.13 |
-1.2 |
-0.50 |
0.72 |
-0.02 |
-1.51 |
0.14 |
-0.85 |
0.71 |
-1.32 |
1.46 |
-0.21 |
-0.27 |
0.76 |
-0.88 |
0.07 |
-0.49 |
0.26 |
0.50 |
0.16 |
-1.86 |
0.49 |
0.16 |
X-squared = 158.4854, df = 172, p = 0.7619
correlation_table(majors_demographics$international_status, majors_demographics$division)
| Domestic |
8368 |
8284 |
5640 |
20754 |
12582 |
37710 |
| International |
561 |
516 |
321 |
1260 |
811 |
2344 |
| Domestic |
9.0 |
8.9 |
6.0 |
22.2 |
13.5 |
40.4 |
100 |
93338 |
| International |
9.7 |
8.9 |
5.5 |
21.7 |
14.0 |
40.3 |
100 |
5813 |
| All |
9.0 |
8.9 |
6.0 |
22.2 |
13.5 |
40.4 |
100 |
99151 |
| Domestic |
93.7 |
94.1 |
94.6 |
94.3 |
93.9 |
94.1 |
94.1 |
| International |
6.3 |
5.9 |
5.4 |
5.7 |
6.1 |
5.9 |
5.9 |
| Total |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
| n |
8929.0 |
8800.0 |
5961.0 |
22014.0 |
13393.0 |
40054.0 |
99151.0 |
| Domestic |
-0.41 |
0 |
0.38 |
0.21 |
-0.23 |
0.02 |
| International |
1.64 |
0 |
-1.52 |
-0.85 |
0.92 |
-0.09 |
X-squared = 7.002, df = 5, p = 0.2205
Socioeconomic Status
The insignificant p-value means that there was no clear pattern
between socioeconomic status and major. Thus, socioeconomic status did
not impact the algorithm for major choice.
correlation_table(majors_demographics$socioeconomic_status, majors_demographics$major)
| Higher income |
78 |
32 |
66 |
47 |
40 |
55 |
54 |
67 |
36 |
26 |
62 |
66 |
69 |
42 |
33 |
29 |
63 |
40 |
74 |
53 |
34 |
44 |
75 |
53 |
56 |
52 |
27 |
42 |
76 |
49 |
49 |
80 |
57 |
55 |
54 |
64 |
66 |
44 |
35 |
68 |
50 |
41 |
50 |
23 |
80 |
59 |
65 |
67 |
47 |
69 |
27 |
33 |
26 |
50 |
38 |
80 |
45 |
30 |
36 |
63 |
35 |
70 |
68 |
58 |
70 |
65 |
39 |
35 |
60 |
48 |
85 |
65 |
83 |
63 |
51 |
33 |
48 |
23 |
52 |
27 |
47 |
36 |
66 |
68 |
59 |
47 |
26 |
62 |
32 |
39 |
42 |
63 |
35 |
64 |
70 |
64 |
68 |
59 |
26 |
51 |
45 |
77 |
70 |
27 |
40 |
67 |
28 |
37 |
70 |
34 |
37 |
79 |
18 |
64 |
20 |
16 |
28 |
34 |
57 |
52 |
38 |
57 |
36 |
55 |
35 |
44 |
50 |
56 |
53 |
68 |
47 |
22 |
50 |
31 |
36 |
81 |
64 |
44 |
57 |
44 |
38 |
31 |
54 |
62 |
59 |
71 |
28 |
66 |
53 |
43 |
89 |
52 |
85 |
41 |
39 |
30 |
41 |
75 |
26 |
46 |
59 |
70 |
19 |
53 |
36 |
60 |
46 |
65 |
55 |
62 |
30 |
56 |
40 |
| In poverty |
195 |
80 |
147 |
112 |
61 |
120 |
119 |
152 |
75 |
83 |
144 |
135 |
143 |
146 |
65 |
54 |
152 |
103 |
163 |
106 |
73 |
102 |
148 |
140 |
136 |
143 |
85 |
87 |
158 |
150 |
130 |
163 |
134 |
107 |
116 |
135 |
127 |
94 |
86 |
176 |
126 |
101 |
83 |
53 |
156 |
133 |
140 |
162 |
94 |
133 |
64 |
87 |
64 |
150 |
91 |
162 |
80 |
86 |
87 |
160 |
87 |
126 |
159 |
142 |
164 |
160 |
76 |
81 |
135 |
103 |
146 |
180 |
139 |
122 |
108 |
81 |
118 |
60 |
127 |
68 |
124 |
93 |
169 |
131 |
136 |
120 |
96 |
126 |
69 |
80 |
124 |
146 |
112 |
127 |
133 |
137 |
139 |
149 |
68 |
127 |
124 |
193 |
164 |
73 |
68 |
164 |
47 |
84 |
156 |
66 |
114 |
138 |
51 |
104 |
52 |
47 |
69 |
108 |
130 |
96 |
90 |
121 |
80 |
94 |
90 |
65 |
117 |
129 |
96 |
147 |
104 |
70 |
98 |
64 |
65 |
173 |
123 |
80 |
134 |
95 |
61 |
73 |
119 |
167 |
135 |
160 |
55 |
111 |
118 |
75 |
167 |
90 |
182 |
81 |
79 |
61 |
90 |
146 |
68 |
96 |
172 |
166 |
54 |
158 |
91 |
127 |
123 |
114 |
114 |
169 |
84 |
120 |
100 |
| Lower-middle income |
120 |
46 |
116 |
76 |
57 |
88 |
84 |
107 |
72 |
55 |
100 |
96 |
102 |
96 |
56 |
64 |
99 |
47 |
128 |
88 |
44 |
90 |
96 |
118 |
102 |
99 |
69 |
49 |
112 |
99 |
90 |
133 |
80 |
94 |
70 |
96 |
95 |
69 |
66 |
135 |
72 |
71 |
64 |
55 |
114 |
110 |
99 |
125 |
72 |
124 |
54 |
55 |
49 |
106 |
74 |
135 |
48 |
51 |
64 |
121 |
55 |
103 |
115 |
113 |
142 |
121 |
60 |
38 |
112 |
66 |
123 |
135 |
117 |
92 |
83 |
64 |
103 |
48 |
87 |
51 |
107 |
75 |
105 |
90 |
93 |
102 |
69 |
109 |
54 |
59 |
79 |
107 |
93 |
90 |
99 |
117 |
109 |
108 |
56 |
79 |
100 |
146 |
97 |
63 |
59 |
104 |
40 |
67 |
110 |
48 |
85 |
87 |
41 |
66 |
48 |
44 |
45 |
73 |
114 |
70 |
69 |
74 |
60 |
90 |
64 |
50 |
79 |
118 |
106 |
106 |
95 |
57 |
78 |
44 |
57 |
138 |
87 |
57 |
88 |
92 |
59 |
55 |
96 |
97 |
101 |
118 |
48 |
87 |
86 |
67 |
136 |
76 |
138 |
77 |
61 |
71 |
76 |
95 |
53 |
80 |
120 |
104 |
42 |
89 |
64 |
111 |
102 |
88 |
76 |
127 |
46 |
94 |
82 |
| Middle income |
273 |
148 |
267 |
197 |
125 |
207 |
238 |
287 |
167 |
139 |
250 |
261 |
255 |
233 |
133 |
133 |
239 |
148 |
302 |
209 |
134 |
208 |
334 |
254 |
260 |
276 |
141 |
163 |
307 |
265 |
253 |
292 |
226 |
242 |
159 |
287 |
256 |
149 |
149 |
296 |
224 |
216 |
198 |
112 |
306 |
251 |
267 |
281 |
171 |
295 |
139 |
162 |
129 |
265 |
189 |
368 |
121 |
130 |
181 |
306 |
159 |
269 |
322 |
246 |
315 |
306 |
175 |
136 |
247 |
191 |
320 |
291 |
289 |
274 |
198 |
132 |
233 |
107 |
190 |
113 |
239 |
215 |
284 |
258 |
252 |
202 |
174 |
248 |
143 |
136 |
213 |
256 |
186 |
237 |
259 |
294 |
252 |
304 |
136 |
237 |
246 |
283 |
306 |
135 |
152 |
284 |
99 |
175 |
261 |
143 |
198 |
217 |
113 |
194 |
100 |
106 |
119 |
206 |
218 |
173 |
201 |
205 |
166 |
193 |
189 |
152 |
188 |
257 |
211 |
301 |
186 |
115 |
181 |
110 |
145 |
323 |
234 |
139 |
227 |
199 |
151 |
133 |
266 |
250 |
244 |
289 |
109 |
214 |
235 |
155 |
297 |
211 |
335 |
182 |
174 |
114 |
155 |
200 |
113 |
218 |
307 |
314 |
88 |
275 |
154 |
255 |
201 |
260 |
217 |
287 |
128 |
219 |
163 |
| Near poverty |
162 |
84 |
140 |
96 |
63 |
95 |
148 |
139 |
75 |
79 |
134 |
144 |
139 |
123 |
57 |
65 |
123 |
92 |
172 |
116 |
64 |
99 |
159 |
116 |
150 |
146 |
72 |
73 |
147 |
172 |
127 |
150 |
140 |
125 |
94 |
143 |
151 |
91 |
82 |
146 |
120 |
109 |
92 |
53 |
156 |
116 |
156 |
153 |
97 |
178 |
75 |
65 |
79 |
154 |
78 |
159 |
70 |
57 |
87 |
146 |
82 |
139 |
138 |
113 |
144 |
169 |
81 |
89 |
161 |
101 |
171 |
145 |
159 |
136 |
97 |
96 |
101 |
46 |
124 |
58 |
129 |
101 |
147 |
138 |
144 |
91 |
103 |
124 |
87 |
72 |
105 |
134 |
107 |
138 |
146 |
152 |
136 |
163 |
90 |
136 |
122 |
164 |
153 |
72 |
75 |
149 |
52 |
89 |
151 |
61 |
106 |
122 |
52 |
113 |
52 |
62 |
76 |
115 |
120 |
95 |
109 |
93 |
77 |
101 |
98 |
73 |
107 |
137 |
120 |
142 |
110 |
66 |
96 |
53 |
71 |
165 |
117 |
64 |
122 |
86 |
65 |
65 |
135 |
145 |
112 |
136 |
53 |
112 |
120 |
84 |
145 |
94 |
158 |
84 |
92 |
60 |
86 |
118 |
47 |
115 |
159 |
173 |
54 |
143 |
108 |
122 |
105 |
128 |
103 |
138 |
60 |
142 |
95 |
| Higher income |
0.9 |
0.4 |
0.8 |
0.5 |
0.5 |
0.6 |
0.6 |
0.8 |
0.4 |
0.3 |
0.7 |
0.8 |
0.8 |
0.5 |
0.4 |
0.3 |
0.7 |
0.5 |
0.8 |
0.6 |
0.4 |
0.5 |
0.9 |
0.6 |
0.6 |
0.6 |
0.3 |
0.5 |
0.9 |
0.6 |
0.6 |
0.9 |
0.7 |
0.6 |
0.6 |
0.7 |
0.8 |
0.5 |
0.4 |
0.8 |
0.6 |
0.5 |
0.6 |
0.3 |
0.9 |
0.7 |
0.7 |
0.8 |
0.5 |
0.8 |
0.3 |
0.4 |
0.3 |
0.6 |
0.4 |
0.9 |
0.5 |
0.3 |
0.4 |
0.7 |
0.4 |
0.8 |
0.8 |
0.7 |
0.8 |
0.7 |
0.4 |
0.4 |
0.7 |
0.5 |
1.0 |
0.7 |
0.9 |
0.7 |
0.6 |
0.4 |
0.5 |
0.3 |
0.6 |
0.3 |
0.5 |
0.4 |
0.8 |
0.8 |
0.7 |
0.5 |
0.3 |
0.7 |
0.4 |
0.4 |
0.5 |
0.7 |
0.4 |
0.7 |
0.8 |
0.7 |
0.8 |
0.7 |
0.3 |
0.6 |
0.5 |
0.9 |
0.8 |
0.3 |
0.5 |
0.8 |
0.3 |
0.4 |
0.8 |
0.4 |
0.4 |
0.9 |
0.2 |
0.7 |
0.2 |
0.2 |
0.3 |
0.4 |
0.7 |
0.6 |
0.4 |
0.7 |
0.4 |
0.6 |
0.4 |
0.5 |
0.6 |
0.6 |
0.6 |
0.8 |
0.5 |
0.3 |
0.6 |
0.4 |
0.4 |
0.9 |
0.7 |
0.5 |
0.7 |
0.5 |
0.4 |
0.4 |
0.6 |
0.7 |
0.7 |
0.8 |
0.3 |
0.8 |
0.6 |
0.5 |
1.0 |
0.6 |
1.0 |
0.5 |
0.4 |
0.3 |
0.5 |
0.9 |
0.3 |
0.5 |
0.7 |
0.8 |
0.2 |
0.6 |
0.4 |
0.7 |
0.5 |
0.7 |
0.6 |
0.7 |
0.3 |
0.6 |
0.5 |
100 |
8741 |
| In poverty |
1.0 |
0.4 |
0.7 |
0.6 |
0.3 |
0.6 |
0.6 |
0.8 |
0.4 |
0.4 |
0.7 |
0.7 |
0.7 |
0.7 |
0.3 |
0.3 |
0.8 |
0.5 |
0.8 |
0.5 |
0.4 |
0.5 |
0.8 |
0.7 |
0.7 |
0.7 |
0.4 |
0.4 |
0.8 |
0.8 |
0.7 |
0.8 |
0.7 |
0.5 |
0.6 |
0.7 |
0.6 |
0.5 |
0.4 |
0.9 |
0.6 |
0.5 |
0.4 |
0.3 |
0.8 |
0.7 |
0.7 |
0.8 |
0.5 |
0.7 |
0.3 |
0.4 |
0.3 |
0.8 |
0.5 |
0.8 |
0.4 |
0.4 |
0.4 |
0.8 |
0.4 |
0.6 |
0.8 |
0.7 |
0.8 |
0.8 |
0.4 |
0.4 |
0.7 |
0.5 |
0.7 |
0.9 |
0.7 |
0.6 |
0.5 |
0.4 |
0.6 |
0.3 |
0.6 |
0.3 |
0.6 |
0.5 |
0.9 |
0.7 |
0.7 |
0.6 |
0.5 |
0.6 |
0.4 |
0.4 |
0.6 |
0.7 |
0.6 |
0.6 |
0.7 |
0.7 |
0.7 |
0.8 |
0.3 |
0.6 |
0.6 |
1.0 |
0.8 |
0.4 |
0.3 |
0.8 |
0.2 |
0.4 |
0.8 |
0.3 |
0.6 |
0.7 |
0.3 |
0.5 |
0.3 |
0.2 |
0.4 |
0.5 |
0.7 |
0.5 |
0.5 |
0.6 |
0.4 |
0.5 |
0.5 |
0.3 |
0.6 |
0.7 |
0.5 |
0.7 |
0.5 |
0.4 |
0.5 |
0.3 |
0.3 |
0.9 |
0.6 |
0.4 |
0.7 |
0.5 |
0.3 |
0.4 |
0.6 |
0.8 |
0.7 |
0.8 |
0.3 |
0.6 |
0.6 |
0.4 |
0.8 |
0.5 |
0.9 |
0.4 |
0.4 |
0.3 |
0.5 |
0.7 |
0.3 |
0.5 |
0.9 |
0.8 |
0.3 |
0.8 |
0.5 |
0.6 |
0.6 |
0.6 |
0.6 |
0.9 |
0.4 |
0.6 |
0.5 |
100 |
19654 |
| Lower-middle income |
0.8 |
0.3 |
0.8 |
0.5 |
0.4 |
0.6 |
0.6 |
0.7 |
0.5 |
0.4 |
0.7 |
0.7 |
0.7 |
0.7 |
0.4 |
0.4 |
0.7 |
0.3 |
0.9 |
0.6 |
0.3 |
0.6 |
0.7 |
0.8 |
0.7 |
0.7 |
0.5 |
0.3 |
0.8 |
0.7 |
0.6 |
0.9 |
0.5 |
0.6 |
0.5 |
0.7 |
0.6 |
0.5 |
0.4 |
0.9 |
0.5 |
0.5 |
0.4 |
0.4 |
0.8 |
0.7 |
0.7 |
0.8 |
0.5 |
0.8 |
0.4 |
0.4 |
0.3 |
0.7 |
0.5 |
0.9 |
0.3 |
0.3 |
0.4 |
0.8 |
0.4 |
0.7 |
0.8 |
0.8 |
1.0 |
0.8 |
0.4 |
0.3 |
0.8 |
0.4 |
0.8 |
0.9 |
0.8 |
0.6 |
0.6 |
0.4 |
0.7 |
0.3 |
0.6 |
0.3 |
0.7 |
0.5 |
0.7 |
0.6 |
0.6 |
0.7 |
0.5 |
0.7 |
0.4 |
0.4 |
0.5 |
0.7 |
0.6 |
0.6 |
0.7 |
0.8 |
0.7 |
0.7 |
0.4 |
0.5 |
0.7 |
1.0 |
0.7 |
0.4 |
0.4 |
0.7 |
0.3 |
0.5 |
0.7 |
0.3 |
0.6 |
0.6 |
0.3 |
0.4 |
0.3 |
0.3 |
0.3 |
0.5 |
0.8 |
0.5 |
0.5 |
0.5 |
0.4 |
0.6 |
0.4 |
0.3 |
0.5 |
0.8 |
0.7 |
0.7 |
0.6 |
0.4 |
0.5 |
0.3 |
0.4 |
0.9 |
0.6 |
0.4 |
0.6 |
0.6 |
0.4 |
0.4 |
0.7 |
0.7 |
0.7 |
0.8 |
0.3 |
0.6 |
0.6 |
0.5 |
0.9 |
0.5 |
0.9 |
0.5 |
0.4 |
0.5 |
0.5 |
0.6 |
0.4 |
0.5 |
0.8 |
0.7 |
0.3 |
0.6 |
0.4 |
0.8 |
0.7 |
0.6 |
0.5 |
0.9 |
0.3 |
0.6 |
0.6 |
100 |
14732 |
| Middle income |
0.7 |
0.4 |
0.7 |
0.5 |
0.3 |
0.6 |
0.6 |
0.8 |
0.5 |
0.4 |
0.7 |
0.7 |
0.7 |
0.6 |
0.4 |
0.4 |
0.6 |
0.4 |
0.8 |
0.6 |
0.4 |
0.6 |
0.9 |
0.7 |
0.7 |
0.8 |
0.4 |
0.4 |
0.8 |
0.7 |
0.7 |
0.8 |
0.6 |
0.7 |
0.4 |
0.8 |
0.7 |
0.4 |
0.4 |
0.8 |
0.6 |
0.6 |
0.5 |
0.3 |
0.8 |
0.7 |
0.7 |
0.8 |
0.5 |
0.8 |
0.4 |
0.4 |
0.4 |
0.7 |
0.5 |
1.0 |
0.3 |
0.4 |
0.5 |
0.8 |
0.4 |
0.7 |
0.9 |
0.7 |
0.9 |
0.8 |
0.5 |
0.4 |
0.7 |
0.5 |
0.9 |
0.8 |
0.8 |
0.7 |
0.5 |
0.4 |
0.6 |
0.3 |
0.5 |
0.3 |
0.6 |
0.6 |
0.8 |
0.7 |
0.7 |
0.5 |
0.5 |
0.7 |
0.4 |
0.4 |
0.6 |
0.7 |
0.5 |
0.6 |
0.7 |
0.8 |
0.7 |
0.8 |
0.4 |
0.6 |
0.7 |
0.8 |
0.8 |
0.4 |
0.4 |
0.8 |
0.3 |
0.5 |
0.7 |
0.4 |
0.5 |
0.6 |
0.3 |
0.5 |
0.3 |
0.3 |
0.3 |
0.6 |
0.6 |
0.5 |
0.5 |
0.6 |
0.5 |
0.5 |
0.5 |
0.4 |
0.5 |
0.7 |
0.6 |
0.8 |
0.5 |
0.3 |
0.5 |
0.3 |
0.4 |
0.9 |
0.6 |
0.4 |
0.6 |
0.5 |
0.4 |
0.4 |
0.7 |
0.7 |
0.7 |
0.8 |
0.3 |
0.6 |
0.6 |
0.4 |
0.8 |
0.6 |
0.9 |
0.5 |
0.5 |
0.3 |
0.4 |
0.5 |
0.3 |
0.6 |
0.8 |
0.9 |
0.2 |
0.7 |
0.4 |
0.7 |
0.5 |
0.7 |
0.6 |
0.8 |
0.3 |
0.6 |
0.4 |
100 |
36774 |
| Near poverty |
0.8 |
0.4 |
0.7 |
0.5 |
0.3 |
0.5 |
0.8 |
0.7 |
0.4 |
0.4 |
0.7 |
0.7 |
0.7 |
0.6 |
0.3 |
0.3 |
0.6 |
0.5 |
0.9 |
0.6 |
0.3 |
0.5 |
0.8 |
0.6 |
0.8 |
0.8 |
0.4 |
0.4 |
0.8 |
0.9 |
0.7 |
0.8 |
0.7 |
0.6 |
0.5 |
0.7 |
0.8 |
0.5 |
0.4 |
0.8 |
0.6 |
0.6 |
0.5 |
0.3 |
0.8 |
0.6 |
0.8 |
0.8 |
0.5 |
0.9 |
0.4 |
0.3 |
0.4 |
0.8 |
0.4 |
0.8 |
0.4 |
0.3 |
0.5 |
0.8 |
0.4 |
0.7 |
0.7 |
0.6 |
0.7 |
0.9 |
0.4 |
0.5 |
0.8 |
0.5 |
0.9 |
0.8 |
0.8 |
0.7 |
0.5 |
0.5 |
0.5 |
0.2 |
0.6 |
0.3 |
0.7 |
0.5 |
0.8 |
0.7 |
0.7 |
0.5 |
0.5 |
0.6 |
0.5 |
0.4 |
0.5 |
0.7 |
0.6 |
0.7 |
0.8 |
0.8 |
0.7 |
0.8 |
0.5 |
0.7 |
0.6 |
0.9 |
0.8 |
0.4 |
0.4 |
0.8 |
0.3 |
0.5 |
0.8 |
0.3 |
0.6 |
0.6 |
0.3 |
0.6 |
0.3 |
0.3 |
0.4 |
0.6 |
0.6 |
0.5 |
0.6 |
0.5 |
0.4 |
0.5 |
0.5 |
0.4 |
0.6 |
0.7 |
0.6 |
0.7 |
0.6 |
0.3 |
0.5 |
0.3 |
0.4 |
0.9 |
0.6 |
0.3 |
0.6 |
0.4 |
0.3 |
0.3 |
0.7 |
0.8 |
0.6 |
0.7 |
0.3 |
0.6 |
0.6 |
0.4 |
0.8 |
0.5 |
0.8 |
0.4 |
0.5 |
0.3 |
0.4 |
0.6 |
0.2 |
0.6 |
0.8 |
0.9 |
0.3 |
0.7 |
0.6 |
0.6 |
0.5 |
0.7 |
0.5 |
0.7 |
0.3 |
0.7 |
0.5 |
100 |
19250 |
| All |
0.8 |
0.4 |
0.7 |
0.5 |
0.3 |
0.6 |
0.6 |
0.8 |
0.4 |
0.4 |
0.7 |
0.7 |
0.7 |
0.6 |
0.3 |
0.3 |
0.7 |
0.4 |
0.8 |
0.6 |
0.4 |
0.5 |
0.8 |
0.7 |
0.7 |
0.7 |
0.4 |
0.4 |
0.8 |
0.7 |
0.7 |
0.8 |
0.6 |
0.6 |
0.5 |
0.7 |
0.7 |
0.5 |
0.4 |
0.8 |
0.6 |
0.5 |
0.5 |
0.3 |
0.8 |
0.7 |
0.7 |
0.8 |
0.5 |
0.8 |
0.4 |
0.4 |
0.3 |
0.7 |
0.5 |
0.9 |
0.4 |
0.4 |
0.5 |
0.8 |
0.4 |
0.7 |
0.8 |
0.7 |
0.8 |
0.8 |
0.4 |
0.4 |
0.7 |
0.5 |
0.9 |
0.8 |
0.8 |
0.7 |
0.5 |
0.4 |
0.6 |
0.3 |
0.6 |
0.3 |
0.7 |
0.5 |
0.8 |
0.7 |
0.7 |
0.6 |
0.5 |
0.7 |
0.4 |
0.4 |
0.6 |
0.7 |
0.5 |
0.7 |
0.7 |
0.8 |
0.7 |
0.8 |
0.4 |
0.6 |
0.6 |
0.9 |
0.8 |
0.4 |
0.4 |
0.8 |
0.3 |
0.5 |
0.8 |
0.4 |
0.5 |
0.6 |
0.3 |
0.5 |
0.3 |
0.3 |
0.3 |
0.5 |
0.6 |
0.5 |
0.5 |
0.6 |
0.4 |
0.5 |
0.5 |
0.4 |
0.5 |
0.7 |
0.6 |
0.8 |
0.5 |
0.3 |
0.5 |
0.3 |
0.4 |
0.9 |
0.6 |
0.4 |
0.6 |
0.5 |
0.4 |
0.4 |
0.7 |
0.7 |
0.7 |
0.8 |
0.3 |
0.6 |
0.6 |
0.4 |
0.8 |
0.5 |
0.9 |
0.5 |
0.4 |
0.3 |
0.5 |
0.6 |
0.3 |
0.6 |
0.8 |
0.8 |
0.3 |
0.7 |
0.5 |
0.7 |
0.6 |
0.7 |
0.6 |
0.8 |
0.4 |
0.6 |
0.5 |
100 |
99151 |
| Higher income |
9.4 |
8.2 |
9.0 |
8.9 |
11.6 |
9.7 |
8.4 |
8.9 |
8.5 |
6.8 |
9.0 |
9.4 |
9.7 |
6.6 |
9.6 |
8.4 |
9.3 |
9.3 |
8.8 |
9.3 |
9.7 |
8.1 |
9.2 |
7.8 |
8.0 |
7.3 |
6.9 |
10.1 |
9.5 |
6.7 |
7.6 |
9.8 |
8.9 |
8.8 |
11.0 |
8.8 |
9.5 |
9.8 |
8.4 |
8.3 |
8.4 |
7.6 |
10.3 |
7.8 |
9.9 |
8.8 |
8.9 |
8.5 |
9.8 |
8.6 |
7.5 |
8.2 |
7.5 |
6.9 |
8.1 |
8.8 |
12.4 |
8.5 |
7.9 |
7.9 |
8.4 |
9.9 |
8.5 |
8.6 |
8.4 |
7.9 |
9.0 |
9.2 |
8.4 |
9.4 |
10.1 |
8.0 |
10.5 |
9.2 |
9.5 |
8.1 |
8.0 |
8.1 |
9.0 |
8.5 |
7.3 |
6.9 |
8.6 |
9.9 |
8.6 |
8.4 |
5.6 |
9.3 |
8.3 |
10.1 |
7.5 |
8.9 |
6.6 |
9.8 |
9.9 |
8.4 |
9.7 |
7.5 |
6.9 |
8.1 |
7.1 |
8.9 |
8.9 |
7.3 |
10.2 |
8.7 |
10.5 |
8.2 |
9.4 |
9.7 |
6.9 |
12.3 |
6.5 |
11.8 |
7.4 |
5.8 |
8.3 |
6.3 |
8.9 |
10.7 |
7.5 |
10.4 |
8.6 |
10.3 |
7.4 |
11.5 |
9.2 |
8.0 |
9.0 |
8.9 |
8.7 |
6.7 |
9.9 |
10.3 |
9.6 |
9.2 |
10.2 |
11.5 |
9.1 |
8.5 |
10.2 |
8.7 |
8.1 |
8.6 |
9.1 |
9.2 |
9.6 |
11.2 |
8.7 |
10.1 |
10.7 |
9.9 |
9.5 |
8.8 |
8.8 |
8.9 |
9.2 |
11.8 |
8.5 |
8.3 |
7.2 |
8.5 |
7.4 |
7.4 |
7.9 |
8.9 |
8.0 |
9.9 |
9.7 |
7.9 |
8.6 |
8.9 |
8.3 |
8.8 |
| In poverty |
23.6 |
20.5 |
20.0 |
21.2 |
17.6 |
21.2 |
18.5 |
20.2 |
17.6 |
21.7 |
20.9 |
19.2 |
20.2 |
22.8 |
18.9 |
15.7 |
22.5 |
24.0 |
19.4 |
18.5 |
20.9 |
18.8 |
18.2 |
20.6 |
19.3 |
20.0 |
21.6 |
21.0 |
19.8 |
20.4 |
20.0 |
19.9 |
21.0 |
17.2 |
23.5 |
18.6 |
18.3 |
21.0 |
20.6 |
21.4 |
21.3 |
18.8 |
17.0 |
17.9 |
19.2 |
19.9 |
19.3 |
20.6 |
19.5 |
16.6 |
17.8 |
21.6 |
18.4 |
20.7 |
19.4 |
17.9 |
22.0 |
24.3 |
19.1 |
20.1 |
20.8 |
17.8 |
19.8 |
21.1 |
19.6 |
19.5 |
17.6 |
21.4 |
18.9 |
20.2 |
17.3 |
22.1 |
17.7 |
17.8 |
20.1 |
20.0 |
19.6 |
21.1 |
21.9 |
21.5 |
19.2 |
17.9 |
21.9 |
19.1 |
19.9 |
21.4 |
20.5 |
18.8 |
17.9 |
20.7 |
22.0 |
20.7 |
21.0 |
19.4 |
18.8 |
17.9 |
19.7 |
19.0 |
18.1 |
20.2 |
19.5 |
22.4 |
20.8 |
19.7 |
17.3 |
21.4 |
17.7 |
18.6 |
20.9 |
18.8 |
21.1 |
21.5 |
18.5 |
19.2 |
19.1 |
17.1 |
20.5 |
20.1 |
20.3 |
19.8 |
17.8 |
22.0 |
19.1 |
17.6 |
18.9 |
16.9 |
21.6 |
18.5 |
16.4 |
19.2 |
19.2 |
21.2 |
19.5 |
21.2 |
17.4 |
19.7 |
19.7 |
20.8 |
21.3 |
18.4 |
16.3 |
20.4 |
17.8 |
23.2 |
20.7 |
20.7 |
18.8 |
18.8 |
19.3 |
17.7 |
20.0 |
17.2 |
20.3 |
17.4 |
17.8 |
18.2 |
20.1 |
23.0 |
22.1 |
17.3 |
21.1 |
20.1 |
21.0 |
22.0 |
20.1 |
18.8 |
21.3 |
17.4 |
20.2 |
21.6 |
24.1 |
19.0 |
20.8 |
19.8 |
| Lower-middle income |
14.5 |
11.8 |
15.8 |
14.4 |
16.5 |
15.6 |
13.1 |
14.2 |
16.9 |
14.4 |
14.5 |
13.7 |
14.4 |
15.0 |
16.3 |
18.6 |
14.6 |
10.9 |
15.3 |
15.4 |
12.6 |
16.6 |
11.8 |
17.3 |
14.5 |
13.8 |
17.5 |
11.8 |
14.0 |
13.5 |
13.9 |
16.3 |
12.6 |
15.1 |
14.2 |
13.2 |
13.7 |
15.4 |
15.8 |
16.4 |
12.2 |
13.2 |
13.1 |
18.6 |
14.0 |
16.4 |
13.6 |
15.9 |
15.0 |
15.5 |
15.0 |
13.7 |
14.1 |
14.6 |
15.7 |
14.9 |
13.2 |
14.4 |
14.1 |
15.2 |
13.2 |
14.6 |
14.3 |
16.8 |
17.0 |
14.7 |
13.9 |
10.0 |
15.7 |
13.0 |
14.6 |
16.5 |
14.9 |
13.4 |
15.5 |
15.8 |
17.1 |
16.9 |
15.0 |
16.1 |
16.6 |
14.4 |
13.6 |
13.1 |
13.6 |
18.1 |
14.7 |
16.3 |
14.0 |
15.3 |
14.0 |
15.2 |
17.4 |
13.7 |
14.0 |
15.3 |
15.5 |
13.8 |
14.9 |
12.5 |
15.7 |
16.9 |
12.3 |
17.0 |
15.0 |
13.5 |
15.0 |
14.8 |
14.7 |
13.6 |
15.7 |
13.5 |
14.9 |
12.2 |
17.6 |
16.0 |
13.4 |
13.6 |
17.8 |
14.4 |
13.6 |
13.5 |
14.3 |
16.9 |
13.4 |
13.0 |
14.6 |
16.9 |
18.1 |
13.9 |
17.5 |
17.3 |
15.5 |
14.6 |
15.2 |
15.7 |
13.9 |
14.8 |
14.0 |
17.8 |
15.8 |
15.4 |
14.3 |
13.5 |
15.5 |
15.2 |
16.4 |
14.7 |
14.1 |
15.8 |
16.3 |
14.5 |
15.4 |
16.6 |
13.7 |
21.1 |
17.0 |
15.0 |
17.3 |
14.4 |
14.7 |
12.6 |
16.3 |
12.4 |
14.1 |
16.4 |
17.7 |
13.4 |
13.5 |
16.2 |
13.2 |
14.9 |
17.1 |
14.9 |
| Middle income |
33.0 |
37.9 |
36.3 |
37.3 |
36.1 |
36.6 |
37.0 |
38.2 |
39.3 |
36.4 |
36.2 |
37.2 |
36.0 |
36.4 |
38.7 |
38.6 |
35.4 |
34.4 |
36.0 |
36.5 |
38.4 |
38.3 |
41.1 |
37.3 |
36.9 |
38.5 |
35.8 |
39.4 |
38.4 |
36.1 |
39.0 |
35.7 |
35.5 |
38.8 |
32.3 |
39.6 |
36.8 |
33.3 |
35.6 |
36.1 |
37.8 |
40.1 |
40.7 |
37.8 |
37.7 |
37.5 |
36.7 |
35.7 |
35.6 |
36.9 |
38.7 |
40.3 |
37.2 |
36.6 |
40.2 |
40.7 |
33.2 |
36.7 |
39.8 |
38.4 |
38.0 |
38.0 |
40.1 |
36.6 |
37.7 |
37.3 |
40.6 |
35.9 |
34.5 |
37.5 |
37.9 |
35.7 |
36.7 |
39.9 |
36.9 |
32.5 |
38.6 |
37.7 |
32.8 |
35.6 |
37.0 |
41.3 |
36.8 |
37.7 |
36.8 |
35.9 |
37.2 |
37.1 |
37.1 |
35.2 |
37.8 |
36.3 |
34.9 |
36.1 |
36.6 |
38.5 |
35.8 |
38.8 |
36.2 |
37.6 |
38.6 |
32.8 |
38.7 |
36.5 |
38.6 |
37.0 |
37.2 |
38.7 |
34.9 |
40.6 |
36.7 |
33.7 |
41.1 |
35.9 |
36.8 |
38.5 |
35.3 |
38.4 |
34.1 |
35.6 |
39.6 |
37.3 |
39.6 |
36.2 |
39.7 |
39.6 |
34.8 |
36.9 |
36.0 |
39.4 |
34.3 |
34.8 |
36.0 |
36.4 |
38.8 |
36.7 |
37.4 |
36.2 |
36.1 |
38.6 |
40.4 |
37.3 |
39.7 |
34.7 |
37.5 |
37.3 |
37.2 |
36.3 |
38.4 |
36.6 |
35.6 |
40.3 |
37.3 |
39.1 |
39.1 |
33.9 |
34.6 |
31.5 |
36.8 |
39.3 |
37.6 |
38.0 |
34.2 |
38.3 |
34.0 |
37.8 |
34.8 |
39.7 |
38.4 |
36.7 |
36.8 |
34.7 |
34.0 |
37.1 |
| Near poverty |
19.6 |
21.5 |
19.0 |
18.2 |
18.2 |
16.8 |
23.0 |
18.5 |
17.6 |
20.7 |
19.4 |
20.5 |
19.6 |
19.2 |
16.6 |
18.8 |
18.2 |
21.4 |
20.5 |
20.3 |
18.3 |
18.2 |
19.6 |
17.0 |
21.3 |
20.4 |
18.3 |
17.6 |
18.4 |
23.4 |
19.6 |
18.3 |
22.0 |
20.1 |
19.1 |
19.7 |
21.7 |
20.4 |
19.6 |
17.8 |
20.3 |
20.3 |
18.9 |
17.9 |
19.2 |
17.3 |
21.5 |
19.4 |
20.2 |
22.3 |
20.9 |
16.2 |
22.8 |
21.2 |
16.6 |
17.6 |
19.2 |
16.1 |
19.1 |
18.3 |
19.6 |
19.7 |
17.2 |
16.8 |
17.2 |
20.6 |
18.8 |
23.5 |
22.5 |
19.8 |
20.2 |
17.8 |
20.2 |
19.8 |
18.1 |
23.6 |
16.7 |
16.2 |
21.4 |
18.3 |
20.0 |
19.4 |
19.1 |
20.1 |
21.1 |
16.2 |
22.0 |
18.5 |
22.6 |
18.7 |
18.7 |
19.0 |
20.1 |
21.0 |
20.7 |
19.9 |
19.3 |
20.8 |
23.9 |
21.6 |
19.2 |
19.0 |
19.4 |
19.5 |
19.0 |
19.4 |
19.5 |
19.7 |
20.2 |
17.3 |
19.6 |
19.0 |
18.9 |
20.9 |
19.1 |
22.5 |
22.6 |
21.5 |
18.8 |
19.5 |
21.5 |
16.9 |
18.4 |
18.9 |
20.6 |
19.0 |
19.8 |
19.7 |
20.5 |
18.6 |
20.3 |
20.0 |
19.1 |
17.5 |
19.0 |
18.8 |
18.7 |
16.7 |
19.4 |
16.7 |
17.4 |
18.2 |
20.1 |
20.1 |
17.2 |
17.6 |
18.1 |
19.0 |
19.6 |
19.8 |
17.4 |
18.0 |
17.6 |
18.1 |
20.7 |
17.9 |
19.2 |
18.6 |
15.3 |
20.7 |
19.5 |
20.9 |
21.0 |
19.9 |
23.8 |
18.1 |
18.2 |
19.5 |
18.2 |
17.6 |
17.2 |
22.5 |
19.8 |
19.4 |
| Total |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
| n |
828.0 |
390.0 |
736.0 |
528.0 |
346.0 |
565.0 |
643.0 |
752.0 |
425.0 |
382.0 |
690.0 |
702.0 |
708.0 |
640.0 |
344.0 |
345.0 |
676.0 |
430.0 |
839.0 |
572.0 |
349.0 |
543.0 |
812.0 |
681.0 |
704.0 |
716.0 |
394.0 |
414.0 |
800.0 |
735.0 |
649.0 |
818.0 |
637.0 |
623.0 |
493.0 |
725.0 |
695.0 |
447.0 |
418.0 |
821.0 |
592.0 |
538.0 |
487.0 |
296.0 |
812.0 |
669.0 |
727.0 |
788.0 |
481.0 |
799.0 |
359.0 |
402.0 |
347.0 |
725.0 |
470.0 |
904.0 |
364.0 |
354.0 |
455.0 |
796.0 |
418.0 |
707.0 |
802.0 |
672.0 |
835.0 |
821.0 |
431.0 |
379.0 |
715.0 |
509.0 |
845.0 |
816.0 |
787.0 |
687.0 |
537.0 |
406.0 |
603.0 |
284.0 |
580.0 |
317.0 |
646.0 |
520.0 |
771.0 |
685.0 |
684.0 |
562.0 |
468.0 |
669.0 |
385.0 |
386.0 |
563.0 |
706.0 |
533.0 |
656.0 |
707.0 |
764.0 |
704.0 |
783.0 |
376.0 |
630.0 |
637.0 |
863.0 |
790.0 |
370.0 |
394.0 |
768.0 |
266.0 |
452.0 |
748.0 |
352.0 |
540.0 |
643.0 |
275.0 |
541.0 |
272.0 |
275.0 |
337.0 |
536.0 |
639.0 |
486.0 |
507.0 |
550.0 |
419.0 |
533.0 |
476.0 |
384.0 |
541.0 |
697.0 |
586.0 |
764.0 |
542.0 |
330.0 |
503.0 |
302.0 |
374.0 |
880.0 |
625.0 |
384.0 |
628.0 |
516.0 |
374.0 |
357.0 |
670.0 |
721.0 |
651.0 |
774.0 |
293.0 |
590.0 |
612.0 |
424.0 |
834.0 |
523.0 |
898.0 |
465.0 |
445.0 |
336.0 |
448.0 |
634.0 |
307.0 |
555.0 |
817.0 |
827.0 |
257.0 |
718.0 |
453.0 |
675.0 |
577.0 |
655.0 |
565.0 |
783.0 |
348.0 |
631.0 |
480.0 |
99151.0 |
| Higher income |
0.59 |
-0.41 |
0.14 |
0.07 |
1.72 |
0.74 |
-0.36 |
0.09 |
-0.24 |
-1.32 |
0.15 |
0.52 |
0.83 |
-1.92 |
0.49 |
-0.26 |
0.44 |
0.34 |
0.00 |
0.36 |
0.58 |
-0.56 |
0.40 |
-0.91 |
-0.77 |
-1.40 |
-1.31 |
0.91 |
0.65 |
-1.96 |
-1.09 |
0.93 |
0.11 |
0.01 |
1.60 |
0.01 |
0.60 |
0.73 |
-0.30 |
-0.51 |
-0.30 |
-0.93 |
1.08 |
-0.61 |
0.99 |
0.00 |
0.11 |
-0.30 |
0.71 |
-0.17 |
-0.83 |
-0.41 |
-0.83 |
-1.74 |
-0.53 |
0.03 |
2.28 |
-0.22 |
-0.65 |
-0.86 |
-0.30 |
0.97 |
-0.32 |
-0.16 |
-0.42 |
-0.87 |
0.16 |
0.27 |
-0.38 |
0.47 |
1.22 |
-0.82 |
1.64 |
0.31 |
0.53 |
-0.47 |
-0.71 |
-0.41 |
0.12 |
-0.18 |
-1.32 |
-1.45 |
-0.24 |
0.98 |
-0.17 |
-0.36 |
-2.38 |
0.39 |
-0.33 |
0.85 |
-1.08 |
0.10 |
-1.75 |
0.81 |
0.97 |
-0.41 |
0.75 |
-1.21 |
-1.24 |
-0.61 |
-1.49 |
0.11 |
0.04 |
-0.98 |
0.89 |
-0.09 |
0.94 |
-0.45 |
0.50 |
0.53 |
-1.54 |
2.96 |
-1.27 |
2.36 |
-0.81 |
-1.67 |
-0.31 |
-1.93 |
0.09 |
1.40 |
-1.00 |
1.22 |
-0.15 |
1.17 |
-1.07 |
1.74 |
0.33 |
-0.69 |
0.19 |
0.08 |
-0.11 |
-1.31 |
0.85 |
0.85 |
0.53 |
0.39 |
1.20 |
1.74 |
0.22 |
-0.22 |
0.88 |
-0.08 |
-0.66 |
-0.20 |
0.21 |
0.33 |
0.43 |
1.94 |
-0.13 |
0.92 |
1.80 |
0.87 |
0.66 |
0.00 |
-0.04 |
0.07 |
0.24 |
2.56 |
-0.20 |
-0.42 |
-1.53 |
-0.34 |
-0.77 |
-1.29 |
-0.62 |
0.06 |
-0.68 |
0.95 |
0.74 |
-0.85 |
-0.12 |
0.05 |
-0.36 |
| In poverty |
2.41 |
0.31 |
0.09 |
0.72 |
-0.92 |
0.76 |
-0.75 |
0.24 |
-1.01 |
0.84 |
0.62 |
-0.35 |
0.22 |
1.70 |
-0.39 |
-1.74 |
1.56 |
1.92 |
-0.26 |
-0.69 |
0.46 |
-0.54 |
-1.02 |
0.43 |
-0.30 |
0.09 |
0.78 |
0.54 |
-0.05 |
0.36 |
0.12 |
0.07 |
0.69 |
-1.48 |
1.85 |
-0.73 |
-0.92 |
0.57 |
0.35 |
1.04 |
0.80 |
-0.55 |
-1.38 |
-0.74 |
-0.39 |
0.03 |
-0.34 |
0.46 |
-0.14 |
-2.02 |
-0.85 |
0.82 |
-0.58 |
0.52 |
-0.22 |
-1.28 |
0.92 |
1.89 |
-0.34 |
0.18 |
0.46 |
-1.19 |
0.00 |
0.76 |
-0.12 |
-0.21 |
-1.02 |
0.68 |
-0.57 |
0.21 |
-1.66 |
1.43 |
-1.36 |
-1.22 |
0.15 |
0.06 |
-0.14 |
0.49 |
1.12 |
0.65 |
-0.36 |
-0.99 |
1.31 |
-0.41 |
0.04 |
0.81 |
0.34 |
-0.57 |
-0.84 |
0.40 |
1.17 |
0.51 |
0.62 |
-0.27 |
-0.60 |
-1.17 |
-0.05 |
-0.50 |
-0.76 |
0.19 |
-0.20 |
1.68 |
0.59 |
-0.04 |
-1.14 |
0.95 |
-0.79 |
-0.59 |
0.63 |
-0.45 |
0.67 |
0.93 |
-0.48 |
-0.31 |
-0.26 |
-1.02 |
0.27 |
0.17 |
0.30 |
-0.03 |
-1.05 |
1.15 |
-0.34 |
-1.13 |
-0.45 |
-1.27 |
0.94 |
-0.78 |
-1.87 |
-0.36 |
-0.33 |
0.57 |
-0.17 |
0.53 |
-1.06 |
-0.11 |
-0.08 |
0.44 |
0.85 |
-0.72 |
-1.53 |
0.27 |
-1.20 |
2.01 |
0.52 |
0.53 |
-0.40 |
-0.55 |
-0.30 |
-0.99 |
0.13 |
-1.34 |
0.30 |
-1.16 |
-0.98 |
-0.69 |
0.13 |
1.81 |
0.92 |
-1.34 |
0.79 |
0.16 |
0.43 |
1.31 |
0.13 |
-0.59 |
0.81 |
-1.39 |
0.19 |
1.11 |
1.81 |
-0.45 |
0.50 |
| Lower-middle income |
-0.27 |
-1.57 |
0.64 |
-0.28 |
0.78 |
0.44 |
-1.18 |
-0.45 |
1.11 |
-0.23 |
-0.25 |
-0.81 |
-0.31 |
0.09 |
0.68 |
1.78 |
-0.14 |
-2.11 |
0.30 |
0.33 |
-1.09 |
1.04 |
-2.24 |
1.67 |
-0.25 |
-0.72 |
1.37 |
-1.60 |
-0.63 |
-0.98 |
-0.65 |
1.04 |
-1.51 |
0.15 |
-0.38 |
-1.13 |
-0.81 |
0.32 |
0.49 |
1.18 |
-1.70 |
-1.00 |
-0.98 |
1.66 |
-0.61 |
1.06 |
-0.87 |
0.73 |
0.06 |
0.48 |
0.09 |
-0.61 |
-0.36 |
-0.17 |
0.50 |
0.06 |
-0.83 |
-0.22 |
-0.44 |
0.25 |
-0.90 |
-0.20 |
-0.38 |
1.32 |
1.61 |
-0.09 |
-0.50 |
-2.44 |
0.56 |
-1.11 |
-0.23 |
1.25 |
0.01 |
-1.00 |
0.36 |
0.47 |
1.42 |
0.89 |
0.09 |
0.57 |
1.12 |
-0.26 |
-0.89 |
-1.17 |
-0.86 |
2.02 |
-0.06 |
0.96 |
-0.42 |
0.22 |
-0.51 |
0.21 |
1.55 |
-0.76 |
-0.59 |
0.33 |
0.43 |
-0.77 |
0.02 |
-1.51 |
0.55 |
1.57 |
-1.88 |
1.08 |
0.06 |
-0.95 |
0.08 |
-0.02 |
-0.11 |
-0.59 |
0.53 |
-0.87 |
0.02 |
-1.60 |
1.19 |
0.49 |
-0.72 |
-0.74 |
1.96 |
-0.26 |
-0.73 |
-0.85 |
-0.29 |
1.21 |
-0.80 |
-0.93 |
-0.15 |
1.42 |
2.03 |
-0.71 |
1.61 |
1.14 |
0.38 |
-0.13 |
0.19 |
0.63 |
-0.61 |
-0.01 |
-0.55 |
1.75 |
0.46 |
0.27 |
-0.36 |
-0.98 |
0.43 |
0.28 |
0.68 |
-0.07 |
-0.52 |
0.50 |
1.09 |
-0.19 |
0.40 |
0.95 |
-0.63 |
2.98 |
1.16 |
0.08 |
1.09 |
-0.27 |
-0.13 |
-1.70 |
0.62 |
-1.71 |
-0.40 |
1.07 |
1.76 |
-0.94 |
-0.87 |
0.99 |
-0.79 |
0.03 |
1.26 |
| Middle income |
-1.95 |
0.28 |
-0.36 |
0.08 |
-0.29 |
-0.18 |
-0.03 |
0.48 |
0.75 |
-0.23 |
-0.37 |
0.04 |
-0.47 |
-0.28 |
0.48 |
0.45 |
-0.74 |
-0.91 |
-0.52 |
-0.22 |
0.40 |
0.47 |
1.89 |
0.09 |
-0.07 |
0.64 |
-0.42 |
0.76 |
0.60 |
-0.46 |
0.79 |
-0.65 |
-0.67 |
0.72 |
-1.76 |
1.10 |
-0.11 |
-1.30 |
-0.48 |
-0.49 |
0.30 |
1.17 |
1.29 |
0.21 |
0.28 |
0.18 |
-0.16 |
-0.66 |
-0.55 |
-0.08 |
0.51 |
1.06 |
0.03 |
-0.24 |
1.11 |
1.79 |
-1.21 |
-0.11 |
0.94 |
0.63 |
0.32 |
0.42 |
1.42 |
-0.21 |
0.30 |
0.09 |
1.20 |
-0.39 |
-1.12 |
0.16 |
0.37 |
-0.67 |
-0.17 |
1.20 |
-0.08 |
-1.51 |
0.63 |
0.16 |
-1.71 |
-0.42 |
-0.04 |
1.59 |
-0.12 |
0.25 |
-0.11 |
-0.45 |
0.03 |
-0.01 |
0.02 |
-0.60 |
0.29 |
-0.36 |
-0.83 |
-0.40 |
-0.20 |
0.63 |
-0.56 |
0.80 |
-0.29 |
0.22 |
0.63 |
-2.07 |
0.76 |
-0.19 |
0.49 |
-0.05 |
0.03 |
0.57 |
-0.99 |
1.09 |
-0.16 |
-1.39 |
1.09 |
-0.47 |
-0.09 |
0.40 |
-0.54 |
0.51 |
-1.23 |
-0.54 |
0.95 |
0.07 |
0.85 |
-0.33 |
0.94 |
0.80 |
-0.89 |
-0.09 |
-0.43 |
1.05 |
-1.06 |
-0.67 |
-0.41 |
-0.19 |
0.53 |
-0.19 |
0.14 |
-0.29 |
-0.39 |
0.55 |
1.04 |
0.05 |
1.11 |
-1.06 |
0.16 |
0.11 |
0.03 |
-0.33 |
0.53 |
-0.18 |
-0.70 |
1.22 |
0.11 |
0.73 |
0.70 |
-0.95 |
-0.87 |
-2.29 |
-0.08 |
0.85 |
0.23 |
0.42 |
-0.75 |
0.53 |
-1.08 |
0.29 |
-0.89 |
1.10 |
0.51 |
-0.20 |
-0.09 |
-0.98 |
-1.13 |
| Near poverty |
0.10 |
0.95 |
-0.24 |
-0.64 |
-0.51 |
-1.40 |
2.07 |
-0.58 |
-0.83 |
0.56 |
0.00 |
0.66 |
0.13 |
-0.11 |
-1.20 |
-0.24 |
-0.72 |
0.93 |
0.71 |
0.47 |
-0.46 |
-0.63 |
0.11 |
-1.41 |
1.14 |
0.59 |
-0.51 |
-0.82 |
-0.67 |
2.45 |
0.09 |
-0.70 |
1.47 |
0.37 |
-0.18 |
0.19 |
1.38 |
0.45 |
0.09 |
-1.06 |
0.47 |
0.45 |
-0.26 |
-0.59 |
-0.13 |
-1.22 |
1.25 |
0.00 |
0.37 |
1.84 |
0.63 |
-1.48 |
1.42 |
1.12 |
-1.39 |
-1.25 |
-0.08 |
-1.41 |
-0.14 |
-0.69 |
0.09 |
0.15 |
-1.42 |
-1.53 |
-1.42 |
0.76 |
-0.29 |
1.80 |
1.88 |
0.22 |
0.54 |
-1.07 |
0.50 |
0.23 |
-0.71 |
1.93 |
-1.49 |
-1.23 |
1.07 |
-0.45 |
0.32 |
0.00 |
-0.22 |
0.43 |
0.97 |
-1.73 |
1.27 |
-0.52 |
1.42 |
-0.34 |
-0.41 |
-0.26 |
0.35 |
0.94 |
0.75 |
0.30 |
-0.06 |
0.89 |
1.99 |
1.24 |
-0.15 |
-0.27 |
-0.03 |
0.02 |
-0.17 |
-0.01 |
0.05 |
0.13 |
0.48 |
-0.89 |
0.11 |
-0.25 |
-0.19 |
0.78 |
-0.11 |
1.18 |
1.31 |
1.07 |
-0.36 |
0.07 |
1.07 |
-1.33 |
-0.48 |
-0.24 |
0.58 |
-0.18 |
0.19 |
0.14 |
0.58 |
-0.52 |
0.47 |
0.24 |
-0.17 |
-0.74 |
-0.19 |
-0.45 |
-0.39 |
-1.22 |
0.01 |
-1.42 |
-0.89 |
-0.52 |
0.43 |
0.42 |
-1.28 |
-1.16 |
-0.52 |
-0.24 |
0.11 |
0.19 |
-1.33 |
-0.75 |
-1.24 |
-0.66 |
0.60 |
-0.65 |
-0.10 |
-0.46 |
-1.63 |
0.70 |
0.03 |
0.98 |
0.58 |
0.31 |
2.14 |
-0.79 |
-0.66 |
0.07 |
-0.64 |
-1.14 |
-0.92 |
1.76 |
0.19 |
X-squared = 667.995, df = 688, p = 0.7008
correlation_table(majors_demographics$socioeconomic_status, majors_demographics$division)
| Higher income |
829 |
785 |
487 |
1873 |
1193 |
3574 |
| In poverty |
1781 |
1778 |
1173 |
4324 |
2599 |
7999 |
| Lower-middle income |
1325 |
1340 |
893 |
3282 |
2020 |
5872 |
| Middle income |
3267 |
3227 |
2220 |
8225 |
5061 |
14774 |
| Near poverty |
1727 |
1670 |
1188 |
4310 |
2520 |
7835 |
| Higher income |
9.5 |
9.0 |
5.6 |
21.4 |
13.6 |
40.9 |
100 |
8741 |
| In poverty |
9.1 |
9.0 |
6.0 |
22.0 |
13.2 |
40.7 |
100 |
19654 |
| Lower-middle income |
9.0 |
9.1 |
6.1 |
22.3 |
13.7 |
39.9 |
100 |
14732 |
| Middle income |
8.9 |
8.8 |
6.0 |
22.4 |
13.8 |
40.2 |
100 |
36774 |
| Near poverty |
9.0 |
8.7 |
6.2 |
22.4 |
13.1 |
40.7 |
100 |
19250 |
| All |
9.0 |
8.9 |
6.0 |
22.2 |
13.5 |
40.4 |
100 |
99151 |
| Higher income |
9.3 |
8.9 |
8.2 |
8.5 |
8.9 |
8.9 |
8.8 |
| In poverty |
19.9 |
20.2 |
19.7 |
19.6 |
19.4 |
20.0 |
19.8 |
| Lower-middle income |
14.8 |
15.2 |
15.0 |
14.9 |
15.1 |
14.7 |
14.9 |
| Middle income |
36.6 |
36.7 |
37.2 |
37.4 |
37.8 |
36.9 |
37.1 |
| Near poverty |
19.3 |
19.0 |
19.9 |
19.6 |
18.8 |
19.6 |
19.4 |
| Total |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
| n |
8929.0 |
8800.0 |
5961.0 |
22014.0 |
13393.0 |
40054.0 |
99151.0 |
| Higher income |
1.49 |
0.33 |
-1.68 |
-1.54 |
0.36 |
0.72 |
| In poverty |
0.26 |
0.81 |
-0.25 |
-0.60 |
-1.08 |
0.67 |
| Lower-middle income |
-0.05 |
0.90 |
0.25 |
0.19 |
0.67 |
-1.03 |
| Middle income |
-0.78 |
-0.64 |
0.19 |
0.67 |
1.33 |
-0.67 |
| Near poverty |
-0.16 |
-0.93 |
0.90 |
0.55 |
-1.57 |
0.66 |
X-squared = 21.984, df = 20, p = 0.3414
Gender
Gender is different than the other demographics because major choice
was explicitly based on the gender ratio. First, we make the same table
from the other demographics, comparing major and gender. Notice that
this is the only demographic with a significant p-value.
correlation_table(majors_demographics$gender, majors_demographics$major)
| Female |
443 |
182 |
441 |
187 |
152 |
237 |
399 |
467 |
208 |
168 |
336 |
423 |
435 |
429 |
157 |
154 |
361 |
203 |
479 |
261 |
211 |
258 |
441 |
330 |
366 |
314 |
275 |
250 |
468 |
522 |
340 |
492 |
391 |
370 |
196 |
323 |
306 |
199 |
155 |
381 |
218 |
311 |
349 |
104 |
421 |
353 |
411 |
559 |
286 |
395 |
238 |
302 |
97 |
314 |
221 |
585 |
150 |
104 |
197 |
489 |
217 |
398 |
536 |
337 |
402 |
487 |
284 |
121 |
440 |
263 |
439 |
512 |
354 |
426 |
317 |
261 |
282 |
119 |
286 |
150 |
410 |
283 |
384 |
418 |
442 |
379 |
299 |
336 |
268 |
123 |
225 |
443 |
334 |
389 |
405 |
474 |
486 |
466 |
305 |
396 |
288 |
513 |
423 |
157 |
172 |
376 |
81 |
271 |
276 |
84 |
402 |
418 |
91 |
327 |
105 |
75 |
204 |
310 |
308 |
282 |
208 |
213 |
209 |
365 |
334 |
226 |
316 |
375 |
375 |
411 |
265 |
61 |
289 |
82 |
232 |
576 |
424 |
232 |
285 |
302 |
115 |
246 |
413 |
353 |
345 |
428 |
165 |
288 |
347 |
195 |
414 |
334 |
563 |
245 |
267 |
266 |
284 |
372 |
203 |
297 |
552 |
476 |
83 |
472 |
240 |
404 |
386 |
315 |
203 |
482 |
165 |
412 |
301 |
| Male |
332 |
192 |
252 |
324 |
183 |
304 |
204 |
242 |
194 |
205 |
316 |
238 |
238 |
170 |
174 |
179 |
276 |
210 |
313 |
286 |
114 |
269 |
336 |
322 |
303 |
377 |
90 |
145 |
296 |
166 |
282 |
287 |
204 |
215 |
283 |
370 |
366 |
231 |
248 |
410 |
362 |
191 |
93 |
178 |
342 |
289 |
278 |
180 |
172 |
363 |
96 |
80 |
240 |
383 |
232 |
253 |
203 |
241 |
236 |
268 |
177 |
273 |
211 |
292 |
394 |
290 |
127 |
247 |
243 |
223 |
356 |
264 |
394 |
225 |
189 |
127 |
291 |
155 |
270 |
155 |
198 |
191 |
351 |
220 |
204 |
139 |
139 |
308 |
94 |
253 |
316 |
231 |
170 |
241 |
266 |
255 |
180 |
284 |
46 |
198 |
314 |
312 |
317 |
197 |
210 |
349 |
178 |
153 |
444 |
262 |
104 |
191 |
174 |
190 |
154 |
195 |
119 |
200 |
300 |
177 |
275 |
312 |
191 |
140 |
109 |
132 |
200 |
294 |
180 |
321 |
249 |
259 |
184 |
214 |
106 |
253 |
167 |
131 |
305 |
191 |
249 |
85 |
218 |
336 |
276 |
302 |
102 |
285 |
240 |
208 |
375 |
157 |
291 |
188 |
152 |
40 |
135 |
236 |
83 |
237 |
217 |
293 |
163 |
196 |
206 |
241 |
150 |
311 |
337 |
260 |
171 |
187 |
152 |
| Nonbinary |
53 |
16 |
43 |
17 |
11 |
24 |
40 |
43 |
23 |
9 |
38 |
41 |
35 |
41 |
13 |
12 |
39 |
17 |
47 |
25 |
24 |
16 |
35 |
29 |
35 |
25 |
29 |
19 |
36 |
47 |
27 |
39 |
42 |
38 |
14 |
32 |
23 |
17 |
15 |
30 |
12 |
36 |
45 |
14 |
49 |
27 |
38 |
49 |
23 |
41 |
25 |
20 |
10 |
28 |
17 |
66 |
11 |
9 |
22 |
39 |
24 |
36 |
55 |
43 |
39 |
44 |
20 |
11 |
32 |
23 |
50 |
40 |
39 |
36 |
31 |
18 |
30 |
10 |
24 |
12 |
38 |
46 |
36 |
47 |
38 |
44 |
30 |
25 |
23 |
10 |
22 |
32 |
29 |
26 |
36 |
35 |
38 |
33 |
25 |
36 |
35 |
38 |
50 |
16 |
12 |
43 |
7 |
28 |
28 |
6 |
34 |
34 |
10 |
24 |
13 |
5 |
14 |
26 |
31 |
27 |
24 |
25 |
19 |
28 |
33 |
26 |
25 |
28 |
31 |
32 |
28 |
10 |
30 |
6 |
36 |
51 |
34 |
21 |
38 |
23 |
10 |
26 |
39 |
32 |
30 |
44 |
26 |
17 |
25 |
21 |
45 |
32 |
44 |
32 |
26 |
30 |
29 |
26 |
21 |
21 |
48 |
58 |
11 |
50 |
7 |
30 |
41 |
29 |
25 |
41 |
12 |
32 |
27 |
| Female |
0.8 |
0.3 |
0.8 |
0.3 |
0.3 |
0.4 |
0.7 |
0.9 |
0.4 |
0.3 |
0.6 |
0.8 |
0.8 |
0.8 |
0.3 |
0.3 |
0.7 |
0.4 |
0.9 |
0.5 |
0.4 |
0.5 |
0.8 |
0.6 |
0.7 |
0.6 |
0.5 |
0.5 |
0.9 |
1.0 |
0.6 |
0.9 |
0.7 |
0.7 |
0.4 |
0.6 |
0.6 |
0.4 |
0.3 |
0.7 |
0.4 |
0.6 |
0.6 |
0.2 |
0.8 |
0.7 |
0.8 |
1.0 |
0.5 |
0.7 |
0.4 |
0.6 |
0.2 |
0.6 |
0.4 |
1.1 |
0.3 |
0.2 |
0.4 |
0.9 |
0.4 |
0.7 |
1.0 |
0.6 |
0.7 |
0.9 |
0.5 |
0.2 |
0.8 |
0.5 |
0.8 |
0.9 |
0.7 |
0.8 |
0.6 |
0.5 |
0.5 |
0.2 |
0.5 |
0.3 |
0.8 |
0.5 |
0.7 |
0.8 |
0.8 |
0.7 |
0.6 |
0.6 |
0.5 |
0.2 |
0.4 |
0.8 |
0.6 |
0.7 |
0.7 |
0.9 |
0.9 |
0.9 |
0.6 |
0.7 |
0.5 |
0.9 |
0.8 |
0.3 |
0.3 |
0.7 |
0.1 |
0.5 |
0.5 |
0.2 |
0.7 |
0.8 |
0.2 |
0.6 |
0.2 |
0.1 |
0.4 |
0.6 |
0.6 |
0.5 |
0.4 |
0.4 |
0.4 |
0.7 |
0.6 |
0.4 |
0.6 |
0.7 |
0.7 |
0.8 |
0.5 |
0.1 |
0.5 |
0.2 |
0.4 |
1.1 |
0.8 |
0.4 |
0.5 |
0.6 |
0.2 |
0.5 |
0.8 |
0.7 |
0.6 |
0.8 |
0.3 |
0.5 |
0.6 |
0.4 |
0.8 |
0.6 |
1.0 |
0.5 |
0.5 |
0.5 |
0.5 |
0.7 |
0.4 |
0.5 |
1.0 |
0.9 |
0.2 |
0.9 |
0.4 |
0.7 |
0.7 |
0.6 |
0.4 |
0.9 |
0.3 |
0.8 |
0.6 |
100 |
54284 |
| Male |
0.8 |
0.5 |
0.6 |
0.8 |
0.5 |
0.8 |
0.5 |
0.6 |
0.5 |
0.5 |
0.8 |
0.6 |
0.6 |
0.4 |
0.4 |
0.4 |
0.7 |
0.5 |
0.8 |
0.7 |
0.3 |
0.7 |
0.8 |
0.8 |
0.8 |
0.9 |
0.2 |
0.4 |
0.7 |
0.4 |
0.7 |
0.7 |
0.5 |
0.5 |
0.7 |
0.9 |
0.9 |
0.6 |
0.6 |
1.0 |
0.9 |
0.5 |
0.2 |
0.4 |
0.9 |
0.7 |
0.7 |
0.5 |
0.4 |
0.9 |
0.2 |
0.2 |
0.6 |
1.0 |
0.6 |
0.6 |
0.5 |
0.6 |
0.6 |
0.7 |
0.4 |
0.7 |
0.5 |
0.7 |
1.0 |
0.7 |
0.3 |
0.6 |
0.6 |
0.6 |
0.9 |
0.7 |
1.0 |
0.6 |
0.5 |
0.3 |
0.7 |
0.4 |
0.7 |
0.4 |
0.5 |
0.5 |
0.9 |
0.6 |
0.5 |
0.3 |
0.3 |
0.8 |
0.2 |
0.6 |
0.8 |
0.6 |
0.4 |
0.6 |
0.7 |
0.6 |
0.5 |
0.7 |
0.1 |
0.5 |
0.8 |
0.8 |
0.8 |
0.5 |
0.5 |
0.9 |
0.4 |
0.4 |
1.1 |
0.7 |
0.3 |
0.5 |
0.4 |
0.5 |
0.4 |
0.5 |
0.3 |
0.5 |
0.8 |
0.4 |
0.7 |
0.8 |
0.5 |
0.4 |
0.3 |
0.3 |
0.5 |
0.7 |
0.5 |
0.8 |
0.6 |
0.7 |
0.5 |
0.5 |
0.3 |
0.6 |
0.4 |
0.3 |
0.8 |
0.5 |
0.6 |
0.2 |
0.5 |
0.8 |
0.7 |
0.8 |
0.3 |
0.7 |
0.6 |
0.5 |
0.9 |
0.4 |
0.7 |
0.5 |
0.4 |
0.1 |
0.3 |
0.6 |
0.2 |
0.6 |
0.5 |
0.7 |
0.4 |
0.5 |
0.5 |
0.6 |
0.4 |
0.8 |
0.8 |
0.7 |
0.4 |
0.5 |
0.4 |
100 |
39845 |
| Nonbinary |
1.1 |
0.3 |
0.9 |
0.3 |
0.2 |
0.5 |
0.8 |
0.9 |
0.5 |
0.2 |
0.8 |
0.8 |
0.7 |
0.8 |
0.3 |
0.2 |
0.8 |
0.3 |
0.9 |
0.5 |
0.5 |
0.3 |
0.7 |
0.6 |
0.7 |
0.5 |
0.6 |
0.4 |
0.7 |
0.9 |
0.5 |
0.8 |
0.8 |
0.8 |
0.3 |
0.6 |
0.5 |
0.3 |
0.3 |
0.6 |
0.2 |
0.7 |
0.9 |
0.3 |
1.0 |
0.5 |
0.8 |
1.0 |
0.5 |
0.8 |
0.5 |
0.4 |
0.2 |
0.6 |
0.3 |
1.3 |
0.2 |
0.2 |
0.4 |
0.8 |
0.5 |
0.7 |
1.1 |
0.9 |
0.8 |
0.9 |
0.4 |
0.2 |
0.6 |
0.5 |
1.0 |
0.8 |
0.8 |
0.7 |
0.6 |
0.4 |
0.6 |
0.2 |
0.5 |
0.2 |
0.8 |
0.9 |
0.7 |
0.9 |
0.8 |
0.9 |
0.6 |
0.5 |
0.5 |
0.2 |
0.4 |
0.6 |
0.6 |
0.5 |
0.7 |
0.7 |
0.8 |
0.7 |
0.5 |
0.7 |
0.7 |
0.8 |
1.0 |
0.3 |
0.2 |
0.9 |
0.1 |
0.6 |
0.6 |
0.1 |
0.7 |
0.7 |
0.2 |
0.5 |
0.3 |
0.1 |
0.3 |
0.5 |
0.6 |
0.5 |
0.5 |
0.5 |
0.4 |
0.6 |
0.7 |
0.5 |
0.5 |
0.6 |
0.6 |
0.6 |
0.6 |
0.2 |
0.6 |
0.1 |
0.7 |
1.0 |
0.7 |
0.4 |
0.8 |
0.5 |
0.2 |
0.5 |
0.8 |
0.6 |
0.6 |
0.9 |
0.5 |
0.3 |
0.5 |
0.4 |
0.9 |
0.6 |
0.9 |
0.6 |
0.5 |
0.6 |
0.6 |
0.5 |
0.4 |
0.4 |
1.0 |
1.2 |
0.2 |
1.0 |
0.1 |
0.6 |
0.8 |
0.6 |
0.5 |
0.8 |
0.2 |
0.6 |
0.5 |
100 |
5022 |
| All |
0.8 |
0.4 |
0.7 |
0.5 |
0.3 |
0.6 |
0.6 |
0.8 |
0.4 |
0.4 |
0.7 |
0.7 |
0.7 |
0.6 |
0.3 |
0.3 |
0.7 |
0.4 |
0.8 |
0.6 |
0.4 |
0.5 |
0.8 |
0.7 |
0.7 |
0.7 |
0.4 |
0.4 |
0.8 |
0.7 |
0.7 |
0.8 |
0.6 |
0.6 |
0.5 |
0.7 |
0.7 |
0.5 |
0.4 |
0.8 |
0.6 |
0.5 |
0.5 |
0.3 |
0.8 |
0.7 |
0.7 |
0.8 |
0.5 |
0.8 |
0.4 |
0.4 |
0.3 |
0.7 |
0.5 |
0.9 |
0.4 |
0.4 |
0.5 |
0.8 |
0.4 |
0.7 |
0.8 |
0.7 |
0.8 |
0.8 |
0.4 |
0.4 |
0.7 |
0.5 |
0.9 |
0.8 |
0.8 |
0.7 |
0.5 |
0.4 |
0.6 |
0.3 |
0.6 |
0.3 |
0.7 |
0.5 |
0.8 |
0.7 |
0.7 |
0.6 |
0.5 |
0.7 |
0.4 |
0.4 |
0.6 |
0.7 |
0.5 |
0.7 |
0.7 |
0.8 |
0.7 |
0.8 |
0.4 |
0.6 |
0.6 |
0.9 |
0.8 |
0.4 |
0.4 |
0.8 |
0.3 |
0.5 |
0.8 |
0.4 |
0.5 |
0.6 |
0.3 |
0.5 |
0.3 |
0.3 |
0.3 |
0.5 |
0.6 |
0.5 |
0.5 |
0.6 |
0.4 |
0.5 |
0.5 |
0.4 |
0.5 |
0.7 |
0.6 |
0.8 |
0.5 |
0.3 |
0.5 |
0.3 |
0.4 |
0.9 |
0.6 |
0.4 |
0.6 |
0.5 |
0.4 |
0.4 |
0.7 |
0.7 |
0.7 |
0.8 |
0.3 |
0.6 |
0.6 |
0.4 |
0.8 |
0.5 |
0.9 |
0.5 |
0.4 |
0.3 |
0.5 |
0.6 |
0.3 |
0.6 |
0.8 |
0.8 |
0.3 |
0.7 |
0.5 |
0.7 |
0.6 |
0.7 |
0.6 |
0.8 |
0.4 |
0.6 |
0.5 |
100 |
99151 |
| Female |
53.5 |
46.7 |
59.9 |
35.4 |
43.9 |
41.9 |
62.1 |
62.1 |
48.9 |
44.0 |
48.7 |
60.3 |
61.4 |
67.0 |
45.6 |
44.6 |
53.4 |
47.2 |
57.1 |
45.6 |
60.5 |
47.5 |
54.3 |
48.5 |
52 |
43.9 |
69.8 |
60.4 |
58.5 |
71.0 |
52.4 |
60.1 |
61.4 |
59.4 |
39.8 |
44.6 |
44.0 |
44.5 |
37.1 |
46.4 |
36.8 |
57.8 |
71.7 |
35.1 |
51.8 |
52.8 |
56.5 |
70.9 |
59.5 |
49.4 |
66.3 |
75.1 |
28.0 |
43.3 |
47.0 |
64.7 |
41.2 |
29.4 |
43.3 |
61.4 |
51.9 |
56.3 |
66.8 |
50.1 |
48.1 |
59.3 |
65.9 |
31.9 |
61.5 |
51.7 |
52.0 |
62.7 |
45.0 |
62.0 |
59.0 |
64.3 |
46.8 |
41.9 |
49.3 |
47.3 |
63.5 |
54.4 |
49.8 |
61.0 |
64.6 |
67.4 |
63.9 |
50.2 |
69.6 |
31.9 |
40.0 |
62.7 |
62.7 |
59.3 |
57.3 |
62.0 |
69.0 |
59.5 |
81.1 |
62.9 |
45.2 |
59.4 |
53.5 |
42.4 |
43.7 |
49.0 |
30.5 |
60.0 |
36.9 |
23.9 |
74.4 |
65.0 |
33.1 |
60.4 |
38.6 |
27.3 |
60.5 |
57.8 |
48.2 |
58.0 |
41.0 |
38.7 |
49.9 |
68.5 |
70.2 |
58.9 |
58.4 |
53.8 |
64.0 |
53.8 |
48.9 |
18.5 |
57.5 |
27.2 |
62.0 |
65.5 |
67.8 |
60.4 |
45.4 |
58.5 |
30.7 |
68.9 |
61.6 |
49.0 |
53.0 |
55.3 |
56.3 |
48.8 |
56.7 |
46.0 |
49.6 |
63.9 |
62.7 |
52.7 |
60.0 |
79.2 |
63.4 |
58.7 |
66.1 |
53.5 |
67.6 |
57.6 |
32.3 |
65.7 |
53.0 |
59.9 |
66.9 |
48.1 |
35.9 |
61.6 |
47.4 |
65.3 |
62.7 |
54.7 |
| Male |
40.1 |
49.2 |
34.2 |
61.4 |
52.9 |
53.8 |
31.7 |
32.2 |
45.6 |
53.7 |
45.8 |
33.9 |
33.6 |
26.6 |
50.6 |
51.9 |
40.8 |
48.8 |
37.3 |
50.0 |
32.7 |
49.5 |
41.4 |
47.3 |
43 |
52.7 |
22.8 |
35.0 |
37.0 |
22.6 |
43.5 |
35.1 |
32.0 |
34.5 |
57.4 |
51.0 |
52.7 |
51.7 |
59.3 |
49.9 |
61.1 |
35.5 |
19.1 |
60.1 |
42.1 |
43.2 |
38.2 |
22.8 |
35.8 |
45.4 |
26.7 |
19.9 |
69.2 |
52.8 |
49.4 |
28.0 |
55.8 |
68.1 |
51.9 |
33.7 |
42.3 |
38.6 |
26.3 |
43.5 |
47.2 |
35.3 |
29.5 |
65.2 |
34.0 |
43.8 |
42.1 |
32.4 |
50.1 |
32.8 |
35.2 |
31.3 |
48.3 |
54.6 |
46.6 |
48.9 |
30.7 |
36.7 |
45.5 |
32.1 |
29.8 |
24.7 |
29.7 |
46.0 |
24.4 |
65.5 |
56.1 |
32.7 |
31.9 |
36.7 |
37.6 |
33.4 |
25.6 |
36.3 |
12.2 |
31.4 |
49.3 |
36.2 |
40.1 |
53.2 |
53.3 |
45.4 |
66.9 |
33.8 |
59.4 |
74.4 |
19.3 |
29.7 |
63.3 |
35.1 |
56.6 |
70.9 |
35.3 |
37.3 |
46.9 |
36.4 |
54.2 |
56.7 |
45.6 |
26.3 |
22.9 |
34.4 |
37.0 |
42.2 |
30.7 |
42.0 |
45.9 |
78.5 |
36.6 |
70.9 |
28.3 |
28.7 |
26.7 |
34.1 |
48.6 |
37.0 |
66.6 |
23.8 |
32.5 |
46.6 |
42.4 |
39.0 |
34.8 |
48.3 |
39.2 |
49.1 |
45.0 |
30.0 |
32.4 |
40.4 |
34.2 |
11.9 |
30.1 |
37.2 |
27.0 |
42.7 |
26.6 |
35.4 |
63.4 |
27.3 |
45.5 |
35.7 |
26.0 |
47.5 |
59.6 |
33.2 |
49.1 |
29.6 |
31.7 |
40.2 |
| Nonbinary |
6.4 |
4.1 |
5.8 |
3.2 |
3.2 |
4.2 |
6.2 |
5.7 |
5.4 |
2.4 |
5.5 |
5.8 |
4.9 |
6.4 |
3.8 |
3.5 |
5.8 |
4.0 |
5.6 |
4.4 |
6.9 |
2.9 |
4.3 |
4.3 |
5 |
3.5 |
7.4 |
4.6 |
4.5 |
6.4 |
4.2 |
4.8 |
6.6 |
6.1 |
2.8 |
4.4 |
3.3 |
3.8 |
3.6 |
3.7 |
2.0 |
6.7 |
9.2 |
4.7 |
6.0 |
4.0 |
5.2 |
6.2 |
4.8 |
5.1 |
7.0 |
5.0 |
2.9 |
3.9 |
3.6 |
7.3 |
3.0 |
2.5 |
4.8 |
4.9 |
5.7 |
5.1 |
6.9 |
6.4 |
4.7 |
5.4 |
4.6 |
2.9 |
4.5 |
4.5 |
5.9 |
4.9 |
5.0 |
5.2 |
5.8 |
4.4 |
5.0 |
3.5 |
4.1 |
3.8 |
5.9 |
8.8 |
4.7 |
6.9 |
5.6 |
7.8 |
6.4 |
3.7 |
6.0 |
2.6 |
3.9 |
4.5 |
5.4 |
4.0 |
5.1 |
4.6 |
5.4 |
4.2 |
6.6 |
5.7 |
5.5 |
4.4 |
6.3 |
4.3 |
3.0 |
5.6 |
2.6 |
6.2 |
3.7 |
1.7 |
6.3 |
5.3 |
3.6 |
4.4 |
4.8 |
1.8 |
4.2 |
4.9 |
4.9 |
5.6 |
4.7 |
4.5 |
4.5 |
5.3 |
6.9 |
6.8 |
4.6 |
4.0 |
5.3 |
4.2 |
5.2 |
3.0 |
6.0 |
2.0 |
9.6 |
5.8 |
5.4 |
5.5 |
6.1 |
4.5 |
2.7 |
7.3 |
5.8 |
4.4 |
4.6 |
5.7 |
8.9 |
2.9 |
4.1 |
5.0 |
5.4 |
6.1 |
4.9 |
6.9 |
5.8 |
8.9 |
6.5 |
4.1 |
6.8 |
3.8 |
5.9 |
7.0 |
4.3 |
7.0 |
1.5 |
4.4 |
7.1 |
4.4 |
4.4 |
5.2 |
3.4 |
5.1 |
5.6 |
5.1 |
| Total |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
| n |
828.0 |
390.0 |
736.0 |
528.0 |
346.0 |
565.0 |
643.0 |
752.0 |
425.0 |
382.0 |
690.0 |
702.0 |
708.0 |
640.0 |
344.0 |
345.0 |
676.0 |
430.0 |
839.0 |
572.0 |
349.0 |
543.0 |
812.0 |
681.0 |
704 |
716.0 |
394.0 |
414.0 |
800.0 |
735.0 |
649.0 |
818.0 |
637.0 |
623.0 |
493.0 |
725.0 |
695.0 |
447.0 |
418.0 |
821.0 |
592.0 |
538.0 |
487.0 |
296.0 |
812.0 |
669.0 |
727.0 |
788.0 |
481.0 |
799.0 |
359.0 |
402.0 |
347.0 |
725.0 |
470.0 |
904.0 |
364.0 |
354.0 |
455.0 |
796.0 |
418.0 |
707.0 |
802.0 |
672.0 |
835.0 |
821.0 |
431.0 |
379.0 |
715.0 |
509.0 |
845.0 |
816.0 |
787.0 |
687.0 |
537.0 |
406.0 |
603.0 |
284.0 |
580.0 |
317.0 |
646.0 |
520.0 |
771.0 |
685.0 |
684.0 |
562.0 |
468.0 |
669.0 |
385.0 |
386.0 |
563.0 |
706.0 |
533.0 |
656.0 |
707.0 |
764.0 |
704.0 |
783.0 |
376.0 |
630.0 |
637.0 |
863.0 |
790.0 |
370.0 |
394.0 |
768.0 |
266.0 |
452.0 |
748.0 |
352.0 |
540.0 |
643.0 |
275.0 |
541.0 |
272.0 |
275.0 |
337.0 |
536.0 |
639.0 |
486.0 |
507.0 |
550.0 |
419.0 |
533.0 |
476.0 |
384.0 |
541.0 |
697.0 |
586.0 |
764.0 |
542.0 |
330.0 |
503.0 |
302.0 |
374.0 |
880.0 |
625.0 |
384.0 |
628.0 |
516.0 |
374.0 |
357.0 |
670.0 |
721.0 |
651.0 |
774.0 |
293.0 |
590.0 |
612.0 |
424.0 |
834.0 |
523.0 |
898.0 |
465.0 |
445.0 |
336.0 |
448.0 |
634.0 |
307.0 |
555.0 |
817.0 |
827.0 |
257.0 |
718.0 |
453.0 |
675.0 |
577.0 |
655.0 |
565.0 |
783.0 |
348.0 |
631.0 |
480.0 |
99151.0 |
| Female |
-0.48 |
-2.16 |
1.90 |
-6.00 |
-2.72 |
-4.11 |
2.50 |
2.72 |
-1.62 |
-2.84 |
-2.15 |
1.97 |
2.41 |
4.20 |
-2.28 |
-2.54 |
-0.47 |
-2.11 |
0.92 |
-2.95 |
1.44 |
-2.28 |
-0.17 |
-2.22 |
-0.99 |
-3.94 |
4.04 |
1.55 |
1.43 |
5.96 |
-0.81 |
2.09 |
2.26 |
1.57 |
-4.50 |
-3.71 |
-3.82 |
-2.92 |
-4.88 |
-3.23 |
-5.89 |
0.96 |
5.04 |
-4.56 |
-1.12 |
-0.69 |
0.65 |
6.14 |
1.40 |
-2.03 |
2.96 |
5.52 |
-6.75 |
-4.16 |
-2.26 |
4.05 |
-3.49 |
-6.45 |
-3.30 |
2.55 |
-0.78 |
0.56 |
4.63 |
-1.61 |
-2.58 |
1.77 |
3.13 |
-6.00 |
2.45 |
-0.94 |
-1.10 |
3.09 |
-3.70 |
2.57 |
1.34 |
2.60 |
-2.65 |
-2.93 |
-1.77 |
-1.79 |
2.99 |
-0.10 |
-1.86 |
2.22 |
3.49 |
4.07 |
2.67 |
-1.58 |
3.94 |
-6.08 |
-4.74 |
2.87 |
2.47 |
1.57 |
0.91 |
2.72 |
5.12 |
1.80 |
6.91 |
2.75 |
-3.25 |
1.86 |
-0.46 |
-3.20 |
-2.98 |
-2.17 |
-5.36 |
1.50 |
-6.60 |
-7.83 |
6.19 |
3.52 |
-4.85 |
1.79 |
-3.60 |
-6.16 |
1.44 |
0.97 |
-2.24 |
0.98 |
-4.18 |
-5.08 |
-1.35 |
4.28 |
4.55 |
1.09 |
1.15 |
-0.34 |
3.02 |
-0.36 |
-1.84 |
-8.90 |
0.82 |
-6.48 |
1.90 |
4.29 |
4.42 |
1.50 |
-3.17 |
1.16 |
-6.27 |
3.62 |
2.41 |
-2.10 |
-0.60 |
0.21 |
0.36 |
-1.95 |
0.65 |
-2.44 |
-1.99 |
2.82 |
3.22 |
-0.60 |
1.50 |
6.05 |
2.47 |
1.34 |
2.69 |
-0.39 |
4.95 |
1.09 |
-4.86 |
3.98 |
-0.51 |
1.79 |
3.94 |
-2.30 |
-6.05 |
2.58 |
-1.85 |
3.58 |
2.36 |
| Male |
-0.04 |
2.82 |
-2.55 |
7.68 |
3.73 |
5.11 |
-3.38 |
-3.46 |
1.78 |
4.16 |
2.32 |
-2.63 |
-2.76 |
-5.44 |
3.04 |
3.43 |
0.26 |
2.83 |
-1.32 |
3.70 |
-2.22 |
3.44 |
0.54 |
2.92 |
1.19 |
5.26 |
-5.43 |
-1.66 |
-1.42 |
-7.53 |
1.31 |
-2.30 |
-3.25 |
-2.23 |
6.03 |
4.61 |
5.19 |
3.83 |
6.17 |
4.41 |
8.05 |
-1.71 |
-7.34 |
5.41 |
0.87 |
1.23 |
-0.83 |
-7.68 |
-1.53 |
2.34 |
-4.02 |
-6.42 |
8.52 |
5.37 |
3.14 |
-5.79 |
4.69 |
8.28 |
3.93 |
-2.90 |
0.70 |
-0.66 |
-6.20 |
1.34 |
3.19 |
-2.20 |
-3.51 |
7.67 |
-2.62 |
1.29 |
0.89 |
-3.53 |
4.37 |
-3.07 |
-1.82 |
-2.83 |
3.13 |
3.83 |
2.42 |
2.45 |
-3.82 |
-1.24 |
2.34 |
-3.33 |
-4.27 |
-5.78 |
-3.58 |
2.39 |
-4.88 |
7.86 |
5.97 |
-3.13 |
-3.02 |
-1.39 |
-1.07 |
-2.97 |
-6.12 |
-1.73 |
-8.55 |
-3.47 |
3.63 |
-1.87 |
-0.03 |
3.96 |
4.11 |
2.30 |
6.88 |
-2.13 |
8.27 |
10.14 |
-7.67 |
-4.19 |
6.04 |
-1.86 |
4.27 |
8.04 |
-1.41 |
-1.05 |
2.70 |
-1.31 |
4.99 |
6.12 |
1.74 |
-5.07 |
-5.95 |
-1.80 |
-1.18 |
0.83 |
-3.62 |
0.80 |
2.11 |
10.97 |
-1.28 |
8.41 |
-3.61 |
-5.35 |
-5.31 |
-1.88 |
3.31 |
-1.14 |
8.05 |
-4.88 |
-3.12 |
2.72 |
0.89 |
-0.51 |
-1.45 |
3.11 |
-0.38 |
2.88 |
2.18 |
-3.67 |
-3.68 |
0.08 |
-2.01 |
-8.18 |
-3.36 |
-1.18 |
-3.63 |
0.94 |
-6.14 |
-2.16 |
5.88 |
-5.45 |
1.78 |
-1.84 |
-5.38 |
2.95 |
7.30 |
-3.08 |
2.63 |
-4.18 |
-2.94 |
| Nonbinary |
1.71 |
-0.84 |
0.94 |
-1.88 |
-1.56 |
-0.86 |
1.30 |
0.80 |
0.32 |
-2.35 |
0.52 |
0.91 |
-0.14 |
1.51 |
-1.06 |
-1.31 |
0.81 |
-1.02 |
0.69 |
-0.74 |
1.50 |
-2.19 |
-0.96 |
-0.94 |
-0.11 |
-1.87 |
2.02 |
-0.43 |
-0.71 |
1.60 |
-1.02 |
-0.38 |
1.71 |
1.15 |
-2.20 |
-0.78 |
-2.06 |
-1.19 |
-1.34 |
-1.80 |
-3.28 |
1.68 |
4.09 |
-0.26 |
1.23 |
-1.18 |
0.19 |
1.44 |
-0.28 |
0.08 |
1.60 |
-0.08 |
-1.81 |
-1.44 |
-1.39 |
2.99 |
-1.73 |
-2.11 |
-0.22 |
-0.21 |
0.61 |
0.03 |
2.26 |
1.54 |
-0.51 |
0.37 |
-0.39 |
-1.87 |
-0.70 |
-0.55 |
1.10 |
-0.21 |
-0.14 |
0.20 |
0.73 |
-0.57 |
-0.10 |
-1.16 |
-0.99 |
-1.01 |
0.92 |
3.83 |
-0.49 |
2.09 |
0.57 |
2.91 |
1.29 |
-1.53 |
0.79 |
-2.16 |
-1.22 |
-0.63 |
0.39 |
-1.25 |
0.03 |
-0.59 |
0.39 |
-1.06 |
1.36 |
0.72 |
0.48 |
-0.86 |
1.58 |
-0.63 |
-1.78 |
0.66 |
-1.76 |
1.07 |
-1.61 |
-2.80 |
1.27 |
0.25 |
-1.05 |
-0.65 |
-0.21 |
-2.39 |
-0.74 |
-0.22 |
-0.24 |
0.48 |
-0.33 |
-0.54 |
-0.48 |
0.19 |
1.81 |
1.49 |
-0.46 |
-1.23 |
0.24 |
-1.08 |
0.10 |
-1.64 |
0.90 |
-2.38 |
3.92 |
0.96 |
0.42 |
0.35 |
1.10 |
-0.61 |
-2.05 |
1.86 |
0.87 |
-0.75 |
-0.52 |
0.77 |
2.90 |
-2.36 |
-1.08 |
-0.10 |
0.42 |
1.07 |
-0.22 |
1.74 |
0.73 |
3.15 |
1.32 |
-1.08 |
1.38 |
-1.34 |
1.03 |
2.49 |
-0.56 |
2.26 |
-3.33 |
-0.72 |
2.18 |
-0.72 |
-0.68 |
0.21 |
-1.34 |
0.01 |
0.55 |
X-squared = 5425.7226, df = 344, p = < 2.2e-16
correlation_table(majors_demographics$gender, majors_demographics$division)
| Female |
4693 |
5524 |
3857 |
12809 |
8092 |
19309 |
| Male |
3789 |
2771 |
1741 |
8071 |
4486 |
18987 |
| Nonbinary |
447 |
505 |
363 |
1134 |
815 |
1758 |
| Female |
8.6 |
10.2 |
7.1 |
23.6 |
14.9 |
35.6 |
100 |
54284 |
| Male |
9.5 |
7.0 |
4.4 |
20.3 |
11.3 |
47.7 |
100 |
39845 |
| Nonbinary |
8.9 |
10.1 |
7.2 |
22.6 |
16.2 |
35.0 |
100 |
5022 |
| All |
9.0 |
8.9 |
6.0 |
22.2 |
13.5 |
40.4 |
100 |
99151 |
| Female |
52.6 |
62.8 |
64.7 |
58.2 |
60.4 |
48.2 |
54.7 |
| Male |
42.4 |
31.5 |
29.2 |
36.7 |
33.5 |
47.4 |
40.2 |
| Nonbinary |
5.0 |
5.7 |
6.1 |
5.2 |
6.1 |
4.4 |
5.1 |
| Total |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
| n |
8929.0 |
8800.0 |
5961.0 |
22014.0 |
13393.0 |
40054.0 |
99151.0 |
| Female |
-2.80 |
10.17 |
10.39 |
6.89 |
8.87 |
-17.69 |
| Male |
3.35 |
-12.87 |
-13.37 |
-8.25 |
-12.22 |
22.79 |
| Nonbinary |
-0.25 |
2.81 |
3.51 |
0.57 |
5.25 |
-6.01 |
X-squared = 1834.7965, df = 10, p = < 2.2e-16
Gender
Since Gender was the only demographic with a statistically
significant correlation it requires further investigation.
We examine both the observed percent female and the expected percent
female based on the data from Big
Economics.
Gender was the only demographic that was explicitly tied to majors
thus we expect to see gender distribution across majors align with the
real world data.
gender_by_major <- majors_demographics[,c(2,5)] |>
group_by(major) |>
mutate(percent_male = sum(gender == "Male")/n()) |>
mutate(percent_female = sum(gender == "Female")/n()) |>
mutate(percent_nonbinary = sum(gender == "Nonbinary")/n()) |>
mutate(percent_gender_minority = 1-percent_male) |>
distinct(major, .keep_all = TRUE) |>
ungroup() |>
select(-gender) |>
drop_na() |>
arrange(major) |>
# Join the Major Counts dataset
left_join(major_counts, join_by(major))
This is the Chi Squared Goodness of Fit Test comparing the observed
percent female and expected percent female for each major.
# Chi Squared Goodness of Fit Test
observed_gxm <- as.matrix(gender_by_major[,3])
expected_gxm <- as.matrix(gender_by_major[,6])
chisq.test(x = observed_gxm, p = expected_gxm / sum(expected_gxm))
##
## Chi-squared test for given probabilities
##
## data: observed_gxm
## X-squared = 10.418, df = 172, p-value = 1
Since the p-value of the Chi Squared Goodness of Fit Test is one this
means that there is a 100% probability the null hypothesis is true.
Thus, the data generation code worked as intended and distribution of
genders across majors that matches empirical data. This graph shows each
major and compares its observed percent female vs what was expected
based on the national data.
# Calculate the Regression line
reg<-lm(national_percent_female ~ percent_female, data = gender_by_major)
# Make ggplot graph
p <- gender_by_major |>
mutate(percent_female = round(percent_female, 3)) |>
# Make tooltip text
mutate(text = paste("Major: ", major, "\nNumber of Majors: ", major_count, "\nNational Percent Female: ", national_percent_female, "\nObserved Percent Female: ", percent_female, "\nNational Popularity Ranking: ", national_popularity_ranking, sep="")) |>
# Make ggplot
ggplot(aes(x = percent_female,
y = national_percent_female,
color = division,
text=text)) +
geom_point() +
theme_classic() +
scale_color_viridis(discrete=TRUE, guide=FALSE) +
labs(x = "Observed Percent Female",
y = "Expected Percent Female",
color = "Major Division") +
# Add line for y=x, the expected regression line
geom_abline(intercept = 0, slope = 1,
color="gray",
linetype="dashed") +
# Add line for the observed regression line
geom_abline(intercept = reg$coefficients[1], slope = reg$coefficients[2],
color="black",
linetype="dashed")+
xlim(0,1) +
ylim(0,1)
# Make the plot interactive with ggplotly
ggplotly(p, tooltip="text")
Here is a more interactive graph showing the same data. The graph was
inspired by r-graph-gallery.
# Interactive Plot
p <- gender_by_major |>
mutate(percent_female = round(percent_female, 3)) |>
# Reorder countries to having big bubbles on top
arrange(desc(major_count)) |>
mutate(major = factor(major, major)) |>
# Make tooltip text
mutate(text = paste("Major: ", major, "\nNumber of Majors: ", major_count, "\nNational Percent Female: ", national_percent_female, "\nObserved Percent Female: ", percent_female, "\nNational Popularity Ranking: ", national_popularity_ranking, sep="")) |>
# Create ggplot
ggplot( aes(x=percent_female,
y=national_percent_female,
size = major_count,
color = division,
text=text)) +
geom_point(alpha=0.7) +
scale_size(range = c(1.4, 19), name="Number of Majors") +
scale_color_viridis(discrete=TRUE, guide=FALSE) +
theme_ipsum() +
labs(x = "Observed Percent Female",
y = "Expected Percent Female") +
xlim(0,1) +
ylim(0,1)
# Turn ggplot interactive with plotly
pp <- ggplotly(p, tooltip="text")
pp
Major Domination by Gender
In this section, we examine the top ten majors with the highest
percentage of people who identify as men, women, and minoritized
genders.
Male Dominated
This graph shows the top 10 majors dominated by men.
major_gender_dominated_graph(gender_by_major, percent_male, "Percent Male", "Top Male Dominated Majors")

Female Dominated
This graph shows the top 10 majors dominated by women.
major_gender_dominated_graph(gender_by_major, percent_female, "Percent Female", "Top Female Dominated Majors")

Minoritized Gender Dominated
This graph shows the top 10 majors dominated by gender minorities
(Women and Nonbinary People).
major_gender_dominated_graph(gender_by_major, percent_gender_minority, "Percent Gender Minority", "Top Gender Minority Dominated Majors")

Major vs Course Subjects
m_v_cs_df0 <- dataset |>
cbind(split_into_multiple(dataset$major, "major")) |>
mutate(major = NULL)
m_v_cs_df1 <- make_demographic_dataframe(m_v_cs_df0, c("major_1", "major_2"), "major", dem_cols = c(3:6,16), rm.na = TRUE, list("course_subjects")) |>
group_by(major) |>
# Remove students without a major
mutate(major = case_when(major == "" ~ NA, .default = major)) |>
drop_na() |>
ungroup()
m_v_cs_df1 <- m_v_cs_df1|>
# Previous Course Subjects
cbind(split_into_multiple(m_v_cs_df1$course_subjects, "course_subjects")) |>
mutate(course_subjects = NULL)
m_v_cs_df2 <- make_demographic_dataframe(m_v_cs_df1, c(6:10), "course_subjects", dem_cols = c(1:5), rm.na = TRUE, list("major")) |>
group_by(course_subjects) |>
# Remove students without who have taken no courses
mutate(course_subjects = case_when(course_subjects == "" ~ NA, .default = course_subjects)) |>
drop_na() |>
ungroup()
chisq.test(m_v_cs_df2$major, m_v_cs_df2$course_subjects)
##
## Pearson's Chi-squared test
##
## data: m_v_cs_df2$major and m_v_cs_df2$course_subjects
## X-squared = 467215, df = 42828, p-value < 2.2e-16
Learning Style
The code used in my summer research to determine the learning
style(s) for each student (observation) is shown here. Notice that no
features are direct inputs to the function. Here are the demographics
generated for each student:
- Ethnoracial Group
- Gender
- International Student Status
- Socioeconomic Status
The function assigned a learning style based on the real world
distribution of learning styles taken from Looney
& Yannelis (2018).
def generate_learning_style(self):
"""
Generate a learning style for the student.
:return: A learning style.
"""
learning_style = [self.assign_demographic(self.learning_style_stats, self.learning_style_dist)]
if np.random.rand() < 0.1:
extra_style = self.assign_demographic(self.learning_style_stats, self.learning_style_dist)
if extra_style not in learning_style:
learning_style.append(extra_style)
return learning_style
Learning Style Demographics
First, we need to generate the
learning_style_demographics using the
make_demographic_dataframe function. Then, we compare the
expected percentage of people with specific learning styles and the
observed percent of people with specific learning styles.
# Make the Learning Style Demographics Dataset
learning_style_demographics <- make_demographic_dataframe(dataset_split, c("learning_style_1", "learning_style_2"), "learning_style", rm.na = TRUE)
# Make the Counts Dataset
learning_style_counts <- chisq_df_generation(learning_style_demographics, .column = learning_style, expected_percent = c(23.56, 28.01, 21.16, 27.27))
This is the Chi Squared Goodness of Fit Test comparing the observed
percent female and expected percent female for each major.
# Chi Squared Goodness of Fit Test
chisq.test(x = learning_style_counts$observed_percent, p = learning_style_counts$expected_percent / sum(learning_style_counts$expected_percent))
##
## Chi-squared test for given probabilities
##
## data: learning_style_counts$observed_percent
## X-squared = 6.0338e-06, df = 3, p-value = 1
Since the p-value of the Chi Squared Goodness of Fit Test is one this
means that there is a 100% probability the null hypothesis is true.
Thus, the data generation code worked as intended and produced a
learning style distribution that matches empirical data.
Demographic Correlations
Ethnoracial Group
correlation_table(learning_style_demographics$ethnoracial_group, learning_style_demographics$learning_style)
| African American or Black |
3362 |
3974 |
2978 |
3868 |
| American Indian or Alaska Native |
167 |
192 |
176 |
199 |
| Asian American |
1899 |
2259 |
1738 |
2182 |
| European American or white |
13392 |
16202 |
12170 |
15735 |
| Latino/a/x American |
5381 |
6135 |
4729 |
5975 |
| Multiracial |
1080 |
1259 |
992 |
1198 |
| Pacific Islander |
63 |
91 |
66 |
82 |
| African American or Black |
23.7 |
28.0 |
21.0 |
27.3 |
100 |
14182 |
| American Indian or Alaska Native |
22.8 |
26.2 |
24.0 |
27.1 |
100 |
734 |
| Asian American |
23.5 |
28.0 |
21.5 |
27.0 |
100 |
8078 |
| European American or white |
23.3 |
28.2 |
21.2 |
27.4 |
100 |
57499 |
| Latino/a/x American |
24.2 |
27.6 |
21.3 |
26.9 |
100 |
22220 |
| Multiracial |
23.8 |
27.8 |
21.9 |
26.5 |
100 |
4529 |
| Pacific Islander |
20.9 |
30.1 |
21.9 |
27.2 |
100 |
302 |
| All |
23.6 |
28.0 |
21.2 |
27.2 |
100 |
107544 |
| African American or Black |
13.3 |
13.2 |
13.0 |
13.2 |
13.2 |
| American Indian or Alaska Native |
0.7 |
0.6 |
0.8 |
0.7 |
0.7 |
| Asian American |
7.5 |
7.5 |
7.6 |
7.5 |
7.5 |
| European American or white |
52.8 |
53.8 |
53.3 |
53.8 |
53.5 |
| Latino/a/x American |
21.2 |
20.4 |
20.7 |
20.4 |
20.7 |
| Multiracial |
4.3 |
4.2 |
4.3 |
4.1 |
4.2 |
| Pacific Islander |
0.2 |
0.3 |
0.3 |
0.3 |
0.3 |
| Total |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
| n |
25344.0 |
30112.0 |
22849.0 |
29239.0 |
107544.0 |
| African American or Black |
0.34 |
0.05 |
-0.64 |
0.20 |
| American Indian or Alaska Native |
-0.45 |
-0.94 |
1.61 |
-0.04 |
| Asian American |
-0.11 |
-0.06 |
0.52 |
-0.30 |
| European American or white |
-1.36 |
0.81 |
-0.42 |
0.82 |
| Latino/a/x American |
2.00 |
-1.10 |
0.12 |
-0.85 |
| Multiracial |
0.39 |
-0.26 |
0.96 |
-0.95 |
| Pacific Islander |
-0.97 |
0.70 |
0.23 |
-0.01 |
X-squared = 17.428, df = 18, p = 0.4939
Gender Group
correlation_table(learning_style_demographics$gender, learning_style_demographics$learning_style)
| Female |
13874 |
16486 |
12576 |
15968 |
| Male |
10201 |
12120 |
9134 |
11786 |
| Nonbinary |
1269 |
1506 |
1139 |
1485 |
| Female |
23.6 |
28.0 |
21.3 |
27.1 |
100 |
58904 |
| Male |
23.6 |
28.0 |
21.1 |
27.3 |
100 |
43241 |
| Nonbinary |
23.5 |
27.9 |
21.1 |
27.5 |
100 |
5399 |
| All |
23.6 |
28.0 |
21.2 |
27.2 |
100 |
107544 |
| Female |
54.7 |
54.7 |
55 |
54.6 |
54.8 |
| Male |
40.3 |
40.2 |
40 |
40.3 |
40.2 |
| Nonbinary |
5.0 |
5.0 |
5 |
5.1 |
5.0 |
| Total |
100.0 |
100.0 |
100 |
100.0 |
100.0 |
| n |
25344.0 |
30112.0 |
22849 |
29239.0 |
107544.0 |
| Female |
-0.06 |
-0.05 |
0.55 |
-0.37 |
| Male |
0.11 |
0.11 |
-0.55 |
0.27 |
| Nonbinary |
-0.09 |
-0.15 |
-0.24 |
0.45 |
X-squared = 1.1352, df = 6, p = 0.98
International Student Status
correlation_table(learning_style_demographics$international_status, learning_style_demographics$learning_style)
| Domestic |
23894 |
28322 |
21491 |
27497 |
| International |
1450 |
1790 |
1358 |
1742 |
| Domestic |
23.6 |
28.0 |
21.2 |
27.2 |
100 |
101204 |
| International |
22.9 |
28.2 |
21.4 |
27.5 |
100 |
6340 |
| All |
23.6 |
28.0 |
21.2 |
27.2 |
100 |
107544 |
| Domestic |
94.3 |
94.1 |
94.1 |
94 |
94.1 |
| International |
5.7 |
5.9 |
5.9 |
6 |
5.9 |
| Total |
100.0 |
100.0 |
100.0 |
100 |
100.0 |
| n |
25344.0 |
30112.0 |
22849.0 |
29239 |
107544.0 |
| Domestic |
0.29 |
-0.09 |
-0.07 |
-0.11 |
| International |
-1.14 |
0.35 |
0.30 |
0.44 |
X-squared = 1.8158, df = 3, p = 0.6115
Socioeconomic Status
correlation_table(learning_style_demographics$socioeconomic_status, learning_style_demographics$learning_style)
| Higher income |
2314 |
2712 |
1991 |
2563 |
| In poverty |
4992 |
6003 |
4481 |
5813 |
| Lower-middle income |
3700 |
4439 |
3475 |
4409 |
| Middle income |
9402 |
11141 |
8528 |
10875 |
| Near poverty |
4936 |
5817 |
4374 |
5579 |
| Higher income |
24.2 |
28.3 |
20.8 |
26.8 |
100 |
9580 |
| In poverty |
23.4 |
28.2 |
21.0 |
27.3 |
100 |
21289 |
| Lower-middle income |
23.1 |
27.7 |
21.7 |
27.5 |
100 |
16023 |
| Middle income |
23.5 |
27.9 |
21.3 |
27.2 |
100 |
39946 |
| Near poverty |
23.8 |
28.1 |
21.1 |
26.9 |
100 |
20706 |
| All |
23.6 |
28.0 |
21.2 |
27.2 |
100 |
107544 |
| Higher income |
9.1 |
9.0 |
8.7 |
8.8 |
8.9 |
| In poverty |
19.7 |
19.9 |
19.6 |
19.9 |
19.8 |
| Lower-middle income |
14.6 |
14.7 |
15.2 |
15.1 |
14.9 |
| Middle income |
37.1 |
37.0 |
37.3 |
37.2 |
37.1 |
| Near poverty |
19.5 |
19.3 |
19.1 |
19.1 |
19.3 |
| Total |
100.0 |
100.0 |
100.0 |
100.0 |
100.0 |
| n |
25344.0 |
30112.0 |
22849.0 |
29239.0 |
107544.0 |
| Higher income |
1.19 |
0.57 |
-0.98 |
-0.82 |
| In poverty |
-0.35 |
0.55 |
-0.63 |
0.33 |
| Lower-middle income |
-1.24 |
-0.71 |
1.21 |
0.80 |
| Middle income |
-0.12 |
-0.41 |
0.45 |
0.14 |
| Near poverty |
0.81 |
0.25 |
-0.38 |
-0.67 |
X-squared = 10.1437, df = 12, p = 0.6034
Learning Style vs Course Types
ls_v_ct_df0 <- dataset |>
cbind(split_into_multiple(dataset$learning_style, "learning_style")) |>
mutate(learning_style = NULL) |>
mutate(learning_style_1 = as.factor(learning_style_1)) |>
mutate(learning_style_2 = as.factor(learning_style_2))
ls_v_ct_df1 <- make_demographic_dataframe(ls_v_ct_df0, c("learning_style_1", "learning_style_2"), "learning_style", dem_cols = c(3:6,15), rm.na = TRUE, list("course_types"))
ls_v_ct_df1 <- ls_v_ct_df1|>
# Previous Course Types
cbind(split_into_multiple(ls_v_ct_df1$course_types, "course_types")) |>
mutate(course_types = NULL)
ls_v_ct_df2 <- make_demographic_dataframe(ls_v_ct_df1, c(6:39), "course_types", dem_cols = c(1:5), rm.na = TRUE, list("learning_style")) |>
group_by(course_types) |>
# Remove students who have not taken any courses
mutate(course_types = case_when(course_types == "" ~ NA, .default = course_types)) |>
drop_na() |>
ungroup()
chisq.test(ls_v_ct_df2$learning_style, ls_v_ct_df2$course_types)
##
## Pearson's Chi-squared test
##
## data: ls_v_ct_df2$learning_style and ls_v_ct_df2$course_types
## X-squared = 367.28, df = 444, p-value = 0.9967